A convolution kernel based multikernel SVM are used to extract emotion causes . Because traditional convolution kernels do not use lexical information at the terminal nodes of syntactic trees , we modify the kernel function with a synonym based improvement . Even with very limited training data , we can still extract sufficient features for the task . Further , kernel-based algorithms are very modular and flexible . Apart from hyperparameters ( such as learning rates and regularization parameters ) only the kernel function has to be changed when the problem domain changes . The kernel defines the feature space and the algorithm ’s notion of similarity between input objects . In this paper , we introduce a new method of data transformation for finger vein recognition system . Our proposed method uses kernel mapping functions to map the data before performing Principal Component Analysis . Kernel Principal Component Analysis ( KPCA ) is a well-known extension of PCA which is suitable for finding nonlinear patterns as it maps the data nonlinearly . The feature parameters for the spoken digits are extracted using Mel-Frequency Cepstral Coefficient ( MFCC ) and provided as the inputs to the recognition process . Different kernel mapping functions such as : polynomial , sigmoid and Radial Basis Function ( RBF ) are used in order to construct the SVM classification models . The experiments are evaluated and the result obtained withdraws a better accuracy compared to the earlier one which uses HMM model for isolated Odia digit recognition . We propose that the mapping between two dynamic sequences can be modeled with a Kernel-based Hidden Markov model , or KHMM . A KHMM is an HMM for which the kernel-based functions are used to model the state observation density of the joint input and output distribution . Specifically , the state observation density is estimated by employing a likelihood-weighted sampling scheme . In this paper , a kernel-based Fuzzy c-means ( KFCM ) clustering approach is presented for use in situations where such linear separation of regions can not be accomplished . The approach uses kernel-based functions to map the data points from the attribute space to a higher-dimensional space where they can be separated into regions by linear planes . A procedure to determine optimal number of regions with the KFCM approach is suggested . The landslide displacement was strongly influenced by periodic precipitation , reservoir level and groundwater level fluctuations . Primary landslide influencing factors were used as independent variables to predict the displacement using several kernel function types including polynomial function , sigmoid function , and RBF based on SVM model . Prediction results demonstrated that the RBF-SVM with the optimal parameters c of 170 , 0.05 and 0.04 can provide the best predictive accuracy , with the maximum and minimum absolute error values of 9.84 and 0.47 mm , respectively . In contrast to these methods , we frame the feature creation procedure as a representation learning problem . Graph Kernels [ reference ] have been proposed as a way to use relational data as part of the classification process , but are quite slow unless approximated [ reference ] . Our approach is complementary ; instead of encoding the structure as part of a kernel function , we learn a representation which allows them to be used directly as features for any classification method . section : Unsupervised Feature Learning However , the approach used fixed convolutional features , and can not jointly learn the kernel classifier and convolutional filters . showed how to learn a kernel function in an unsupervised manner . There have been other attempts to replace the fully connected layers . In the following we describe how the discriminative null space can be kernelised . Given a kernel function , where maps to an implicit higher dimensional space , we can compute the data kernel matrix for training data as . Now the within - class scatter matrix and total - class scatter matrix can be kernelised as : where is a identity matrix , is a block diagonal matrix with block sizes equal to the number of data points for each class and is a matrix with all entries equal to . We use LTAG based features for the parse reranking task and obtain labeled recall and precision of 89.7%/90.0 % on WSJ section 23 of Penn Treebank for sentences of length ≤ 100 words . Our results show that the use of LTAG based tree kernel gives rise to a 17 % relative difference in f-score improvement over the use of a linear kernel without LTAG based features . In this paper , a support vector machine ( SVM ) with linear kernel function based nonparametric model identification and dynamic matrix control ( SVM_DMC ) technique is presented . First , a step response model involving manipulated variables is obtained via system identification by SVM with linear kernel function according to random test data or manufacturing data . Second , an explicit control law of a receding horizon quadric objective is gotten through the predictive control mechanism . The performance observed is the accuracy . The result shows that SVM , with the use of linear function kernel , outperforms the others algorithms . The SVM best accuracy is 82.35 % . Considering the fact that original histogram of oriented gradients ( HOG ) can not extract the body local features in large image regions , its features are improved when extracted , then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows . Considering speed , we select support vector machine ( SVM ) using linear function kernel as a classifier . Combining with HOG extraction and SVM training , the process includes three steps : features extraction , training and detection . In order to further improve pedestrian detection accuracy and avoid the disadvantage of original histogram of oriented gradients ( HOG ) , differential template , overlap ratio and normalization method and so on are improved when HOG features are extracted , then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows . Considering speed , we select support vector machine ( SVM ) using linear function kernel as a classifier . Multi-scale detection technique and non maxima suppression method are employed for precisely locating the pedestrians in the image . In order to process tongue gestures , it is required to solve the magnetic dipole equation , which is computationally expensive to execute in a wearable embedded hardware . In this paper , a support vector machine with linear kernel-based algorithm is proposed , which uses an additional magnetometer that has 96.1 % accuracy with a 2.2 % sensitivity and 1.8 % specificity among 15 participants , is computationally efficient for mTDS , generates high-performance classification results despite small changes of magnet positions ( −5 to 5 mm ) and orientations ( −10 ° to 10 ° ) from a four-dimensional robot emulated tongue movement data compared to five other algorithms . This algorithm generates more accurate results to control devices independently despite the Midas-touch problem between speech and tongue movements . We base our analysis on the Relevance Vector Machines ( RVM ) technique originally proposed by M. Tipping . It effectively locally maximizes the evidence integral for linear kernel-based models . We extend the RVM technique by considering correlated additive Gaussian observation noise and complex-valued signals . To address the need for a flexible model of the bidirectional reflectance distribution function ( BRDF ) that is also suitable for inversion , the FLAIR Model ( Four-Scale Linear Model for AnIsotropic Reflectance ) has been developed H. P. White et al. ( 2001 ) . Based on the more detailed Four-Scale Model J. M. Chen et al. ( 1997 ) , FLAIR is a linear kernel-like model , developed with the aim of not being limited to specific canopy characteristics or view/illumination geometry , while maintaining a direct relationship between canopy architectural properties and model coefficients . Having been previously demonstrated to have the ability to capture the bi-directional patterns in both forward and inverse modes of calculation , this paper examines the FLAIR model in describing the boreal canopy by applying FLAIR to multiangular data sets obtained by various sensors during BOREAS 1994 . The Four-Scale Model , developed by Chen and Leblanc ( 1997 ) , describes canopy reflectance considering four levels of architecture , distributions of tree crowns , branches , shoots , and leaves . A linear kernel-like model has been developed from this Four-Scale Linear Model for Anisotropic Reflectance ( FLAIR ) . While simplifications are performed , an effort has been made not to limit FLAIR to specific canopy characteristics , while maintaining relationships between modeled coefficients and canopy architecture . SOFSEM 2006 ) proved that Nonblocker has a linear-order kernel . We obtain a linear-order kernel for Directed Nonblocker . Specifically , the brain connectome is reconstructed using white matter fiber tracts from presurgical diffusion tensor imaging . To achieve our objective , a two-stage connectome-based prediction framework is developed that gradually selects a small number of abnormal network connections that contribute to the surgical treatment outcome , and in each stage a linear kernel operation is used to further improve the accuracy of the learned classifier . Using a 10-fold cross validation strategy , the first stage in the connectome-based framework is able to separate patients with TLE from normal controls with 80 % accuracy , and second stage in the connectome-based framework is able to correctly predict the surgical treatment outcome of patients with TLE with 70 % accuracy . Different from the studies related with the Down Sydrome , feature selection process is applied before PCA according to the correlation between components of feature vectors . Best results are achieved with euclidean distance metric for kNN and linear kernel type for SVM . In this way , we developed an efficient system to recognize Down syndrome . The Tight Bound of First Fit Decreasing Bin-Packing Algorithm Is FFD(I)???11/9OPT(I)?+?6/9.- Sequential Vector Packing.- A Tighter Analysis of Set Cover Greedy Algorithm for Test Set.- A More Effective Linear Kernelization for Cluster Editing.- CR-precis : A Deterministic Summary Structure for Update Data Streams.- An Effective Refinement Algorithm Based on Swarm Intelligence for Graph Bipartitioning.- On the Complexity and Approximation of the Min-Sum and Min-Max Disjoint Paths In this paper , we introduce the center-constrained MEB problem and subsequently extend the CVM algorithm . The generalized CVM algorithm can now be used with any linear/nonlinear kernel and can also be applied to kernel methods such as SVR and the ranking SVM . Moreover , like the original CVM , its asymptotic time complexity is again linear in m and its space complexity is independent of m. Experiments show that the generalized CVM has comparable performance with state-of-the-art SVM and SVR implementations , but is faster and produces fewer support vectors on very large data sets section : Experiments on Sentiment Analysis Data Sets We now compare the performance of our proposed DANN algorithm to a standard neural network with one hidden layer ( NN ) described by Equation ( 5 ) , and a Support Vector Machine ( SVM ) with a linear kernel . We compare the algorithms on the Amazon reviews data set , as pre - processed by [ reference ] . This data set includes four domains , each one composed of reviews of a specific kind of product ( books , dvd disks , electronics , and kitchen appliances ) . Assumptions ( IV ) and ( V ) only depend on the top - level kernel K and are easy to satisfy in practice . In particular , they always hold for a smooth translation - invariant kernel , such as the Gaussian , as well as the linear kernel . We are now ready to prove Theorem 1 . Then , LBP features are applied and AdaBoost is used to find the most important features for each expression on essential facial parts . At last , SVM with polynomial kernel is used to classify expressions . The method is evaluated on JAFFE and MMI databases . The early fault diagnosis method for the complex equipment comprises the specific steps : extracting collection signals through a sensor ; carrying out wavelet transform on the extracted collection signals for de-noising ; and establishing a fault diagnosis model based on a BP ( Back Propagation ) neural network and a fault diagnosis model based on an SVM ( Support Vector Machine ) according to mechanical features and circuit features , and carrying out fault diagnosis . A kernel function in the fault diagnosis model based on the SVM is a polynomial kernel function , a radial basis function ( RBF ) or a Sigmoid kernel function . According to the early fault diagnosis method , the fault diagnosis of the complex equipment is divided into a fault diagnosis with the mechanical features and a fault diagnosis with the circuit features , and the fault diagnosis models are established respectively according to the mechanical features and the circuit features , so that mechanical tests are easy , many samples can be obtained , quick convergence can be realized by using the BP neural network , and accuracy is higher ; furthermore , the circuit sample data is less . The obtained models are compared against other proposed techniques to assess performances based on the two measurement metrics : mean absolute error ( MAE ) and root mean square error ( RMSE ) . The experimental results show that our SVM predictor using polynomial kernel function can precisely estimate forest fire damage area with the MAE and RMSE as low as 6.48 and 7.65 , respectively . These errors are less than other techniques reported in the literature . To deal with the computational and storage problem for the large-scale data set , an improved Kernel Principal Component Analysis based on 1-order and 2-order statistical quantity , is proposed By dividing the large scale data set into small subsets , we could treat 1-order and 2-order statistical quantity ( mean and autocorrelation matrix ) of each subset as the special computational unit A novel polynomial-matrix kernel function is also adopted to compute the similarity between the data matrices in place of vectors The proposed method can greatly reduce the size of kernel matrix , which makes its computation possible Its effectiveness is demonstrated by the experimental results on the artificial and real data set . By dividing the large scale data set into small subsets , we could treat the autocorrelation matrix of each subset as the special computational unit . A novel polynomial-matrix kernel function is adopted to compute the similarity between the data matrices in place of vectors . It is also proved that the polynomial kernel is the extreme case of the polynomial-matrix one . By initially dividing the large scale data set into small subsets , we could treat the autocorrelation matrix of each subset as the special computational unit . A novel polynomial-matrix kernel function is then adopted to compute the similarity between the data matrices in place of vectors . It is also proved that the polynomial kernel is the extreme case of the polynomial-matrix one . Using the support vector machine ( SVM ) the classifying models were set up to recognize the leaf spectral reflectance with different DD . The numerical experimental results showed that the recognition precisions were very high ( using all 10 input vectors , the precisions were beyond 90 % with the polynomial-based kernel function and beyond 97.4 % with the RBF kernel function . ) . When the input vectors were fewer , the classification precisions were lower . Since PSA , volume , density , and smoking features were to be statistically significant , they were chosen for classification . The proposed system was designed with polynomial based kernel function , which had the best performance ( accuracy : 79 % ) . In Turkish Family Health System , family physician to whom patients are applied firstly , would contribute to extract the risk map of illness and direct patients to correct treatments by using expert system such proposed . The results provide both insights into the geometry of nonlinear data embedding and practical guidelines for choosing an appropriate degree for dimension reduction with polynomial kernels . We further comment on the effect of centering kernels on the spectral property of the polynomial kernel operator . © 2013 The ORL and Pain Expression face databases are selected for evaluations . Experimental results indicate our NC-Kernel based methods outperform some RBF or polynomial kernel based algorithms . To avoid this drawback , there exist two different approaches : approximating the Gaussian kernel by a finite support kernel , or defining new kernels with properties closed to the Gaussian . In this paper , we propose a polynomial kernel family with compact support which overcomes the Gaussian practical drawbacks while preserving a large number of the useful Gaussian properties . The new kernels are not obtained by approximating the Gaussian , though they are derived from it . The results obtained are the best accuracy value generated is 76.67 % with LBP parameters used are 256 × 256 cell size and radius 2 . While for SVM parameters use Polynomial kernel type or RBF with OAA multiclass These polynomials are used as kernels in order to form moments , the invariants of which can easily be derived . The resulted scheme permits the usage of any polynomial-like kernel in a unified and consistent way . The resulted moments and moment invariants demonstrate robustness to noisy conditions and high discriminative power . Using a within-subject design we studied how participants used spatial or conceptual distances to generalize and search for correlated rewards in successive multi-armed bandit tasks . Participant behavior indicated sensitivity to both spatial and conceptual distance , and was best captured using a Bayesian model of generalization that formalized distance-dependent generalization and uncertainty-guided exploration as a Gaussian Process regression with a radial basis function kernel . The same Gaussian Process model best captured human search decisions and judgments in both domains , and could simulate realistic learning curves , where we found equivalent levels of generalization in spatial and conceptual tasks . But so far , its implementation can only rely on the experience and the specific sample characteristics without a unified pattern . Thus , this article explored the related theories and research findings of kernel functions , analyzed the classification characteristics of EO-1 Hyperion hyperspectral imagery , and combined a polynomial kernel function with a radial basis kernel function to form a new kernel function model ( PRBF ) . Then , a hyperspectral remote sensing imagery classifier was constructed based on the PRBF model , and a genetic algorithm ( GA ) was used to optimize the SVM parameters . We compare the performance of double SVMbagging with other well-known classifier ensemble methods in condition diagnosis ; the double SVMbagging with the radial basis kernel performed better than other ensemble method and other kernels . We applied the double SVMbagging with radial basis kernel in 15 UCI benchmark datasets and compare it 's accuracy with other ensemble methods e.g. , Bag- ging , Adaboost , Random forest and Rotation Forest . The performance of this method demonstrates that this method can generate significantly lower predic- tion error than Rotation Forest and Adaboost more often than reverse . We employ support vector machine and fractal analysis for gait recognition and test the identification performance . Testing outcomes indicate an overall accuracy of 93.57 % via radial basis function kernel function . These results demonstrate considerable potential in gait identification . Experimental results show that the proposed CWSB features of 12-lead ECG and the SVM classifier are successful for classification of various heart pathologies . The individual accuracy values for MI , HMD and BBB classes are obtained as 98.37 , 97.39 and 96.40 % , respectively , using SVM classifier and radial basis function kernel function . A comparison has also been made with existing 12-lead ECG-based cardiac disease detection techniques . RESULTS Prediction accuracy of Support vector machine , k- nearest neighbor , Probabilistic neural network , Artificial neural network , Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54 % , 88.33 % , 84.30 % , 86.51 % , 79.07 % and 80.08 % respectively . CONCLUSION Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development . In order to improve the mechanical structure of the type of fault resolution precision high voltage circuit breaker spring mechanism , the paper analyzes the characteristics of the circuit breaker and the combination of mechanical vibration signal PSO algorithm ( PSO ) SVM parameter optimization method proposed collaborative dynamic acceleration constant inertia weight particle swarm optimization ( WCPSO ) optimization support vector machine ( SVM ) analysis breaker fault classification parameters and kernel function parameters . The vibration signal circuit breaker empirical mode decomposition , the total intrinsic mode components through energy analysis to obtain the required fault feature vectors and support vector machine as input , the use of dynamic acceleration constant synergy inertia weight PSO support vector machines penalty factor C and radial basis kernel function parameters optimize the fault feature vector signal input test samples after SVM training sample trained optimized for fault classification , fault status classification . The experimental analysis of this method can effectively improve the resolution of the breaker failure signal type Accuracy . Interval data type can be a good way to aggregate large datasets into smaller ones or to represent data with uncertainty . No algorithmic changes are required from the usual case of continuous data other than the modification of the radial basis kernel function evaluation . Thus , kernel-based algorithms can deal easily with interval data . Data instances are then formed out of the learning data . The data instances then enter the SVMs learning process using the stationary Gaussian kernel with a radial basis kernel function ( RBF ) using the multi - class method . Fig . While impressive results have been shown on point estimates , scenarios that induce multi-modal distributions over future sequences remain challenging . Our work addresses these challenges in a Gaussian Latent Variable model for sequence prediction . Our core contribution is a " Best of Many " sample objective that leads to more accurate and more diverse predictions that better capture the true variations in real-world sequence data . We propose a probabilistic approach to modelling course enrollment decisions , drawing inspiration from multi label classification and mixture models . We use ten years of anonymized student transcripts from a large university to construct a Gaussian latent variable model that learns the joint distribution over course enrollments . The models allow for a diverse set of inference queries and robustness to data sparsity . The bounding box parameters are then fed into the proposed efficient multi-sensor target tracking algorithm . This is achieved by modifying the Gaussian mixture-PHD filter ( GM-PHD ) by incorporating a set of class labels that associate a state to a set of sensors . The performance of the proposed method target tracking method is verified using both synthetic and real world data . By showing their equivalence to Spectral Mixture Gaussian processes , we illuminate the underlying model assumptions and provide a general framework for constructing more complex models that better approximate real-world signals . Our interpretation makes it intuitive to inspect , compare , and alter the models since all prior knowledge is encoded in the Gaussian process kernel functions . We utilise a state space representation to perform efficient inference via Kalman smoothing , and we demonstrate how our interpretation allows for efficient parameter learning in the frequency domain . In this paper , we propose a generalization of a region based Bayesian detection-estimation approach that addresses (i)-(ii ) simultaneously as a bilinear inverse problem . The proposed extension relies on a 2-class Gamma-Gaussian prior mixture modeling to classify the voxels of the brain region either as activated or unactivated . Our approach provides both a spatial activity map and a HRF estimation using Monte Carlo Markov chain ( MCMC ) techniques . Firstly we utilize the method of global search to obtain the quick result of the epicenter in the coarse grid condition , and then we determine the final source location just by the local search within the epicenter area with a fine grid . We also employ a 3-D spatial Gaussian filter to obtain the source location and suppress the background noise automatically when the imaging results are scattered or unfocused , getting rid of searching for the major spatial energy points in the conventional least square method . The results based on the synthetic data show that the method combining the varying-grid 3-D Gaussian beam and spatial Gaussian filter can not only ensure the high efficiency with less steps of ray tracing but also can be more adaptable to the low signal-to-noise ratio data and scattered or unfocused imaging energy . The charge assignment and back interpolation methods are currently bottlenecks , and take up to one‐third the execution time of the multigrid method each . Here , we propose alternative approaches to both charge assignment and back interpolation where convolution is used both to map Gaussian representations of atomic charges onto the grid and to map the forces computed at grid points back to atomic positions . These approaches achieve the same force accuracy with reduced run time . We give necessary and sufficient conditions for a Gaussian quantum channel to have a dilation involving a passive , i.e. , number-preserving unitary . We then establish a normal form of such channels : any passively dilatable channel is the result of applying passive unitaries to the input and output of a Gaussian additive channel . To improve the spatial resolution of video , a superresolution reconstruction method based on a sliding window is proposed utilizing the movement information between frames in the low-resolution video . We propose a registration algorithm based on a four-parameter transformation model through Taylor series expansion , using an iterative solving method as well as the Gaussian pyramid image model to estimate the movement parameters from coarseness to fine . Superresolution frames are reconstructed using an iterative back projection ( IBP ) algorithm . The performance of the proposed models is evaluated in various actual cases . The results obtained indicate that the CI-Gaussian model possesses a higher performance in the prediction of the main caving span in actual cases when compared to the other models . These results confirm that it is not possible to consider all the effective parameters in an empirical relationship due to a higher error in the prediction . Invited Lectures.- Grobner Bases : A Short Introduction for Systems Theorists.- Design for Reuse via Structuring Techniques for ASMs.- Computer Aided Systems Theory.- On CAST.FSM Computation of Hierarchical Multi-layer Networks of Automata.- A Holarchical Organized Design Assistant for Hierarchical Decompositions.- Conflict Management in an Intelligent Multiagent Robotics System Based on FSM Approach.- Computer-Aided Simulations of Gaussian Processes and Related Asymptotic Properties.- Hybrid Systems ' Properties - Classification and Relation to Computer Science.- Assertion-Based Analysis of Hybrid Systems with PVS.- Algebraic Description of Physical Systems.- Information Structure and Logical Design of Social Rules.- Towards a Generic Model of Information Processing Systems.- Evolutionary Information and Decision Support Systems : An Integration Based on Ontologies.- A Proposal for Project Management Using CommonKADS.- A General Planning Method for Allocation of Human Resource Groups.- Optimizing Organizational Structure : In the case of binary MNIST , the data points are binarized by setting pixel values larger than 0.5 to 1 , and values smaller than 0.5 to 0 . subsubsection : PixelGAN Autoencoders with Gaussian Prior on MNIST Here we describe the model architecture used for training the PixelGAN autoencoder with a Gaussian prior on the binary MNIST dataset in : mnist ] Figure [ reference ] To endow the network with selective attention without sacrificing the benefits of gradient descent training , we take inspiration from the differentiable attention mechanisms recently used in handwriting synthesis and Neural Turing Machines . Unlike the aforementioned works , we consider an explicitly two - dimensional form of attention , where an array of 2D Gaussian filters is applied to the image , yielding an image ‘ patch ’ of smoothly varying location and zoom . This configuration , which we refer to simply as “ DRAW ” , somewhat resembles the affine transformations used in computer graphics - based autoencoders . For all our experiments on TIMIT , we pretrain the neural network with a Deep Belief Network . Since the inputs are real - valued , the first layer was pre - trained as a Gaussian RBM . Visible biases were initialized to zero and weights to random numbers sampled from a zero - mean normal distribution with standard deviation 0.01 . Next we cap strong gradients using a sigmoid ↦x -/ 2 (+ 1exp (- ⁢ςx )) 1 , to help canceling out effects of varying illumination . We smooth gradients one more time for each orientation with a Gaussian filter of radius ν3 . Finally , the descriptor for each pixel is obtained by the ℓ2 - normalized concatenation of 8 oriented gradients and a ninth small constant value μ . section : Message Passing In the dense CRF formulation , message passing is implemented by applying M Gaussian filters on Q values . Gaussian filter coefficients are derived based on image features such as the pixel locations and RGB values , which reflect how strongly a pixel is related to other pixels . According to the joint representation , we then generate the parameters and , which parameterise the variational distribution over the question semantics . To emphasise , though both and are modelled as parameterised Gaussian distributions , as an approximation only functions during inference by producing samples to compute the stochastic gradients , while is the generative distribution that generates the samples for predicting the question - answer relatedness . Based on the samples , we use SGVB to optimise the lower bound ( Eq . Previous approaches describe data with simple summary statistics or kernel function , and then use statistical tests to determine the difference . However , a summary statistics-based approach can not capture complicated underlying structure , and a kernel function-based approach lacks interpretability of results . RESULTS We propose a novel method D(3)M , for detection of differential distribution of methylation , based on distribution-valued data . CMSM ( Constraint Mutual Subspace Method ) constructs a class subspace for each person and makes the relation between class subspaces by projecting them onto a generalized difference subspace so that the canonical angles between subspaces are enlarged to approach to the orthogonal relation . GDA ( Generalized Discriminant Analysis ) adopts kernel function operator to make it easy to extend and generalize the classical Linear Discriminant Analysis to a non linear one . Both CMSM and GDA are effective to recognize human faces , however , CMSM constructs a subspace from several face images and GDA needs only one face image to perform recognition . The classification principle of SVM is discussed , and three kinds of kernel function are applied to SVM , which are multinomial function , radial basis function , perceptron function . Finally comparison of three kernel function characteristic is presented by three recognition examples : classification of point set in plane , recognition of similar script Chinese characters , and recognition of double screw curve . The rules of kernel function selection in different recognition matter are presented . The algorithm called evolutionary KPCA-LSSVM is the combination of genetic algorithm ( GA ) , kernel principal component analysis ( KPCA ) and Least Squares Support Vector Machine ( LSSVM ) , which can obtain better fault recognition rate . Firstly , kernel function concept is introduced , and then GA is used to select the kernel parameter in order to improve the performances of nonlinear feature extraction and fault classification of KPCA-LSSVM method . Secondly , KPCA is used to extract the nonlinear principal features of loopers by adopting the optimal kernel trick to map nonlinearly the data into a feature space and employing the PCA procedure . This allows one to use fast optimized Poisson solvers while satisfying simultaneously the divergence-free flow state and the no-slip condition at the body surface . To compute the divergence of the momentum equation ( in the PISO loop ) and the interpolation of the fluxes , we propose an hybrid calculation with an analytical resolution ( using the kernel function equation ) of the quantities involving the force term ( singular quantities ) . A careful and original verification study has been carried out which allows to estimate three different errors related to the discretization and to the ( IBM ) . The work that carried out in this paper is to develop a model that can predict better accuracy of churn . We used the existing models of SVM and kernel function of it and collaborated with the artificial neural networks in prediction . The results proved to be outstanding when both the models integrated with each other . The present work proposes a review and comparison of different Kernel functionals and neighborhood geometry for Nonlocal Means ( NLM ) in the task of digital image filtering . Some different alternatives to change the classical exponential kernel function used in NLM methods are explored .
A convolution kernel based multikernel SVM are used to extract emotion causes . Because traditional convolution kernels do not use lexical information at the terminal nodes of syntactic trees , we modify the kernel function with a synonym based improvement . Even with very limited training data , we can still extract sufficient features for the task .
Further , kernel-based algorithms are very modular and flexible . Apart from hyperparameters ( such as learning rates and regularization parameters ) only the kernel function has to be changed when the problem domain changes . The kernel defines the feature space and the algorithm ’s notion of similarity between input objects .
In this paper , we introduce a new method of data transformation for finger vein recognition system . Our proposed method uses kernel mapping functions to map the data before performing Principal Component Analysis . Kernel Principal Component Analysis ( KPCA ) is a well-known extension of PCA which is suitable for finding nonlinear patterns as it maps the data nonlinearly .
The feature parameters for the spoken digits are extracted using Mel-Frequency Cepstral Coefficient ( MFCC ) and provided as the inputs to the recognition process . Different kernel mapping functions such as : polynomial , sigmoid and Radial Basis Function ( RBF ) are used in order to construct the SVM classification models . The experiments are evaluated and the result obtained withdraws a better accuracy compared to the earlier one which uses HMM model for isolated Odia digit recognition .
We propose that the mapping between two dynamic sequences can be modeled with a Kernel-based Hidden Markov model , or KHMM . A KHMM is an HMM for which the kernel-based functions are used to model the state observation density of the joint input and output distribution . Specifically , the state observation density is estimated by employing a likelihood-weighted sampling scheme .
In this paper , a kernel-based Fuzzy c-means ( KFCM ) clustering approach is presented for use in situations where such linear separation of regions can not be accomplished . The approach uses kernel-based functions to map the data points from the attribute space to a higher-dimensional space where they can be separated into regions by linear planes . A procedure to determine optimal number of regions with the KFCM approach is suggested .
The landslide displacement was strongly influenced by periodic precipitation , reservoir level and groundwater level fluctuations . Primary landslide influencing factors were used as independent variables to predict the displacement using several kernel function types including polynomial function , sigmoid function , and RBF based on SVM model . Prediction results demonstrated that the RBF-SVM with the optimal parameters c of 170 , 0.05 and 0.04 can provide the best predictive accuracy , with the maximum and minimum absolute error values of 9.84 and 0.47 mm , respectively .
In contrast to these methods , we frame the feature creation procedure as a representation learning problem . Graph Kernels [ reference ] have been proposed as a way to use relational data as part of the classification process , but are quite slow unless approximated [ reference ] . Our approach is complementary ; instead of encoding the structure as part of a kernel function , we learn a representation which allows them to be used directly as features for any classification method . section : Unsupervised Feature Learning
However , the approach used fixed convolutional features , and can not jointly learn the kernel classifier and convolutional filters . showed how to learn a kernel function in an unsupervised manner . There have been other attempts to replace the fully connected layers .
In the following we describe how the discriminative null space can be kernelised . Given a kernel function , where maps to an implicit higher dimensional space , we can compute the data kernel matrix for training data as . Now the within - class scatter matrix and total - class scatter matrix can be kernelised as : where is a identity matrix , is a block diagonal matrix with block sizes equal to the number of data points for each class and is a matrix with all entries equal to .
We use LTAG based features for the parse reranking task and obtain labeled recall and precision of 89.7%/90.0 % on WSJ section 23 of Penn Treebank for sentences of length ≤ 100 words . Our results show that the use of LTAG based tree kernel gives rise to a 17 % relative difference in f-score improvement over the use of a linear kernel without LTAG based features .
In this paper , a support vector machine ( SVM ) with linear kernel function based nonparametric model identification and dynamic matrix control ( SVM_DMC ) technique is presented . First , a step response model involving manipulated variables is obtained via system identification by SVM with linear kernel function according to random test data or manufacturing data . Second , an explicit control law of a receding horizon quadric objective is gotten through the predictive control mechanism .
The performance observed is the accuracy . The result shows that SVM , with the use of linear function kernel , outperforms the others algorithms . The SVM best accuracy is 82.35 % .
Considering the fact that original histogram of oriented gradients ( HOG ) can not extract the body local features in large image regions , its features are improved when extracted , then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows . Considering speed , we select support vector machine ( SVM ) using linear function kernel as a classifier . Combining with HOG extraction and SVM training , the process includes three steps : features extraction , training and detection .
In order to further improve pedestrian detection accuracy and avoid the disadvantage of original histogram of oriented gradients ( HOG ) , differential template , overlap ratio and normalization method and so on are improved when HOG features are extracted , then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows . Considering speed , we select support vector machine ( SVM ) using linear function kernel as a classifier . Multi-scale detection technique and non maxima suppression method are employed for precisely locating the pedestrians in the image .
In order to process tongue gestures , it is required to solve the magnetic dipole equation , which is computationally expensive to execute in a wearable embedded hardware . In this paper , a support vector machine with linear kernel-based algorithm is proposed , which uses an additional magnetometer that has 96.1 % accuracy with a 2.2 % sensitivity and 1.8 % specificity among 15 participants , is computationally efficient for mTDS , generates high-performance classification results despite small changes of magnet positions ( −5 to 5 mm ) and orientations ( −10 ° to 10 ° ) from a four-dimensional robot emulated tongue movement data compared to five other algorithms . This algorithm generates more accurate results to control devices independently despite the Midas-touch problem between speech and tongue movements .
We base our analysis on the Relevance Vector Machines ( RVM ) technique originally proposed by M. Tipping . It effectively locally maximizes the evidence integral for linear kernel-based models . We extend the RVM technique by considering correlated additive Gaussian observation noise and complex-valued signals .
To address the need for a flexible model of the bidirectional reflectance distribution function ( BRDF ) that is also suitable for inversion , the FLAIR Model ( Four-Scale Linear Model for AnIsotropic Reflectance ) has been developed H. P. White et al. ( 2001 ) . Based on the more detailed Four-Scale Model J. M. Chen et al. ( 1997 ) , FLAIR is a linear kernel-like model , developed with the aim of not being limited to specific canopy characteristics or view/illumination geometry , while maintaining a direct relationship between canopy architectural properties and model coefficients . Having been previously demonstrated to have the ability to capture the bi-directional patterns in both forward and inverse modes of calculation , this paper examines the FLAIR model in describing the boreal canopy by applying FLAIR to multiangular data sets obtained by various sensors during BOREAS 1994 .
The Four-Scale Model , developed by Chen and Leblanc ( 1997 ) , describes canopy reflectance considering four levels of architecture , distributions of tree crowns , branches , shoots , and leaves . A linear kernel-like model has been developed from this Four-Scale Linear Model for Anisotropic Reflectance ( FLAIR ) . While simplifications are performed , an effort has been made not to limit FLAIR to specific canopy characteristics , while maintaining relationships between modeled coefficients and canopy architecture .
SOFSEM 2006 ) proved that Nonblocker has a linear-order kernel . We obtain a linear-order kernel for Directed Nonblocker .
Specifically , the brain connectome is reconstructed using white matter fiber tracts from presurgical diffusion tensor imaging . To achieve our objective , a two-stage connectome-based prediction framework is developed that gradually selects a small number of abnormal network connections that contribute to the surgical treatment outcome , and in each stage a linear kernel operation is used to further improve the accuracy of the learned classifier . Using a 10-fold cross validation strategy , the first stage in the connectome-based framework is able to separate patients with TLE from normal controls with 80 % accuracy , and second stage in the connectome-based framework is able to correctly predict the surgical treatment outcome of patients with TLE with 70 % accuracy .
Different from the studies related with the Down Sydrome , feature selection process is applied before PCA according to the correlation between components of feature vectors . Best results are achieved with euclidean distance metric for kNN and linear kernel type for SVM . In this way , we developed an efficient system to recognize Down syndrome .
The Tight Bound of First Fit Decreasing Bin-Packing Algorithm Is FFD(I)???11/9OPT(I)?+?6/9.- Sequential Vector Packing.- A Tighter Analysis of Set Cover Greedy Algorithm for Test Set.- A More Effective Linear Kernelization for Cluster Editing.- CR-precis : A Deterministic Summary Structure for Update Data Streams.- An Effective Refinement Algorithm Based on Swarm Intelligence for Graph Bipartitioning.- On the Complexity and Approximation of the Min-Sum and Min-Max Disjoint Paths
In this paper , we introduce the center-constrained MEB problem and subsequently extend the CVM algorithm . The generalized CVM algorithm can now be used with any linear/nonlinear kernel and can also be applied to kernel methods such as SVR and the ranking SVM . Moreover , like the original CVM , its asymptotic time complexity is again linear in m and its space complexity is independent of m. Experiments show that the generalized CVM has comparable performance with state-of-the-art SVM and SVR implementations , but is faster and produces fewer support vectors on very large data sets
section : Experiments on Sentiment Analysis Data Sets We now compare the performance of our proposed DANN algorithm to a standard neural network with one hidden layer ( NN ) described by Equation ( 5 ) , and a Support Vector Machine ( SVM ) with a linear kernel . We compare the algorithms on the Amazon reviews data set , as pre - processed by [ reference ] . This data set includes four domains , each one composed of reviews of a specific kind of product ( books , dvd disks , electronics , and kitchen appliances ) .
Assumptions ( IV ) and ( V ) only depend on the top - level kernel K and are easy to satisfy in practice . In particular , they always hold for a smooth translation - invariant kernel , such as the Gaussian , as well as the linear kernel . We are now ready to prove Theorem 1 .
Then , LBP features are applied and AdaBoost is used to find the most important features for each expression on essential facial parts . At last , SVM with polynomial kernel is used to classify expressions . The method is evaluated on JAFFE and MMI databases .
The early fault diagnosis method for the complex equipment comprises the specific steps : extracting collection signals through a sensor ; carrying out wavelet transform on the extracted collection signals for de-noising ; and establishing a fault diagnosis model based on a BP ( Back Propagation ) neural network and a fault diagnosis model based on an SVM ( Support Vector Machine ) according to mechanical features and circuit features , and carrying out fault diagnosis . A kernel function in the fault diagnosis model based on the SVM is a polynomial kernel function , a radial basis function ( RBF ) or a Sigmoid kernel function . According to the early fault diagnosis method , the fault diagnosis of the complex equipment is divided into a fault diagnosis with the mechanical features and a fault diagnosis with the circuit features , and the fault diagnosis models are established respectively according to the mechanical features and the circuit features , so that mechanical tests are easy , many samples can be obtained , quick convergence can be realized by using the BP neural network , and accuracy is higher ; furthermore , the circuit sample data is less .
The obtained models are compared against other proposed techniques to assess performances based on the two measurement metrics : mean absolute error ( MAE ) and root mean square error ( RMSE ) . The experimental results show that our SVM predictor using polynomial kernel function can precisely estimate forest fire damage area with the MAE and RMSE as low as 6.48 and 7.65 , respectively . These errors are less than other techniques reported in the literature .
To deal with the computational and storage problem for the large-scale data set , an improved Kernel Principal Component Analysis based on 1-order and 2-order statistical quantity , is proposed By dividing the large scale data set into small subsets , we could treat 1-order and 2-order statistical quantity ( mean and autocorrelation matrix ) of each subset as the special computational unit A novel polynomial-matrix kernel function is also adopted to compute the similarity between the data matrices in place of vectors The proposed method can greatly reduce the size of kernel matrix , which makes its computation possible Its effectiveness is demonstrated by the experimental results on the artificial and real data set .
By dividing the large scale data set into small subsets , we could treat the autocorrelation matrix of each subset as the special computational unit . A novel polynomial-matrix kernel function is adopted to compute the similarity between the data matrices in place of vectors . It is also proved that the polynomial kernel is the extreme case of the polynomial-matrix one .
By initially dividing the large scale data set into small subsets , we could treat the autocorrelation matrix of each subset as the special computational unit . A novel polynomial-matrix kernel function is then adopted to compute the similarity between the data matrices in place of vectors . It is also proved that the polynomial kernel is the extreme case of the polynomial-matrix one .
Using the support vector machine ( SVM ) the classifying models were set up to recognize the leaf spectral reflectance with different DD . The numerical experimental results showed that the recognition precisions were very high ( using all 10 input vectors , the precisions were beyond 90 % with the polynomial-based kernel function and beyond 97.4 % with the RBF kernel function . ) . When the input vectors were fewer , the classification precisions were lower .
Since PSA , volume , density , and smoking features were to be statistically significant , they were chosen for classification . The proposed system was designed with polynomial based kernel function , which had the best performance ( accuracy : 79 % ) . In Turkish Family Health System , family physician to whom patients are applied firstly , would contribute to extract the risk map of illness and direct patients to correct treatments by using expert system such proposed .
The results provide both insights into the geometry of nonlinear data embedding and practical guidelines for choosing an appropriate degree for dimension reduction with polynomial kernels . We further comment on the effect of centering kernels on the spectral property of the polynomial kernel operator . © 2013
The ORL and Pain Expression face databases are selected for evaluations . Experimental results indicate our NC-Kernel based methods outperform some RBF or polynomial kernel based algorithms .
To avoid this drawback , there exist two different approaches : approximating the Gaussian kernel by a finite support kernel , or defining new kernels with properties closed to the Gaussian . In this paper , we propose a polynomial kernel family with compact support which overcomes the Gaussian practical drawbacks while preserving a large number of the useful Gaussian properties . The new kernels are not obtained by approximating the Gaussian , though they are derived from it .
The results obtained are the best accuracy value generated is 76.67 % with LBP parameters used are 256 × 256 cell size and radius 2 . While for SVM parameters use Polynomial kernel type or RBF with OAA multiclass
These polynomials are used as kernels in order to form moments , the invariants of which can easily be derived . The resulted scheme permits the usage of any polynomial-like kernel in a unified and consistent way . The resulted moments and moment invariants demonstrate robustness to noisy conditions and high discriminative power .
Using a within-subject design we studied how participants used spatial or conceptual distances to generalize and search for correlated rewards in successive multi-armed bandit tasks . Participant behavior indicated sensitivity to both spatial and conceptual distance , and was best captured using a Bayesian model of generalization that formalized distance-dependent generalization and uncertainty-guided exploration as a Gaussian Process regression with a radial basis function kernel . The same Gaussian Process model best captured human search decisions and judgments in both domains , and could simulate realistic learning curves , where we found equivalent levels of generalization in spatial and conceptual tasks .
But so far , its implementation can only rely on the experience and the specific sample characteristics without a unified pattern . Thus , this article explored the related theories and research findings of kernel functions , analyzed the classification characteristics of EO-1 Hyperion hyperspectral imagery , and combined a polynomial kernel function with a radial basis kernel function to form a new kernel function model ( PRBF ) . Then , a hyperspectral remote sensing imagery classifier was constructed based on the PRBF model , and a genetic algorithm ( GA ) was used to optimize the SVM parameters .
We compare the performance of double SVMbagging with other well-known classifier ensemble methods in condition diagnosis ; the double SVMbagging with the radial basis kernel performed better than other ensemble method and other kernels . We applied the double SVMbagging with radial basis kernel in 15 UCI benchmark datasets and compare it 's accuracy with other ensemble methods e.g. , Bag- ging , Adaboost , Random forest and Rotation Forest . The performance of this method demonstrates that this method can generate significantly lower predic- tion error than Rotation Forest and Adaboost more often than reverse .
We employ support vector machine and fractal analysis for gait recognition and test the identification performance . Testing outcomes indicate an overall accuracy of 93.57 % via radial basis function kernel function . These results demonstrate considerable potential in gait identification .
Experimental results show that the proposed CWSB features of 12-lead ECG and the SVM classifier are successful for classification of various heart pathologies . The individual accuracy values for MI , HMD and BBB classes are obtained as 98.37 , 97.39 and 96.40 % , respectively , using SVM classifier and radial basis function kernel function . A comparison has also been made with existing 12-lead ECG-based cardiac disease detection techniques .
RESULTS Prediction accuracy of Support vector machine , k- nearest neighbor , Probabilistic neural network , Artificial neural network , Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54 % , 88.33 % , 84.30 % , 86.51 % , 79.07 % and 80.08 % respectively . CONCLUSION Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development .
In order to improve the mechanical structure of the type of fault resolution precision high voltage circuit breaker spring mechanism , the paper analyzes the characteristics of the circuit breaker and the combination of mechanical vibration signal PSO algorithm ( PSO ) SVM parameter optimization method proposed collaborative dynamic acceleration constant inertia weight particle swarm optimization ( WCPSO ) optimization support vector machine ( SVM ) analysis breaker fault classification parameters and kernel function parameters . The vibration signal circuit breaker empirical mode decomposition , the total intrinsic mode components through energy analysis to obtain the required fault feature vectors and support vector machine as input , the use of dynamic acceleration constant synergy inertia weight PSO support vector machines penalty factor C and radial basis kernel function parameters optimize the fault feature vector signal input test samples after SVM training sample trained optimized for fault classification , fault status classification . The experimental analysis of this method can effectively improve the resolution of the breaker failure signal type Accuracy .
Interval data type can be a good way to aggregate large datasets into smaller ones or to represent data with uncertainty . No algorithmic changes are required from the usual case of continuous data other than the modification of the radial basis kernel function evaluation . Thus , kernel-based algorithms can deal easily with interval data .
Data instances are then formed out of the learning data . The data instances then enter the SVMs learning process using the stationary Gaussian kernel with a radial basis kernel function ( RBF ) using the multi - class method . Fig .
While impressive results have been shown on point estimates , scenarios that induce multi-modal distributions over future sequences remain challenging . Our work addresses these challenges in a Gaussian Latent Variable model for sequence prediction . Our core contribution is a " Best of Many " sample objective that leads to more accurate and more diverse predictions that better capture the true variations in real-world sequence data .
We propose a probabilistic approach to modelling course enrollment decisions , drawing inspiration from multi label classification and mixture models . We use ten years of anonymized student transcripts from a large university to construct a Gaussian latent variable model that learns the joint distribution over course enrollments . The models allow for a diverse set of inference queries and robustness to data sparsity .
The bounding box parameters are then fed into the proposed efficient multi-sensor target tracking algorithm . This is achieved by modifying the Gaussian mixture-PHD filter ( GM-PHD ) by incorporating a set of class labels that associate a state to a set of sensors . The performance of the proposed method target tracking method is verified using both synthetic and real world data .
By showing their equivalence to Spectral Mixture Gaussian processes , we illuminate the underlying model assumptions and provide a general framework for constructing more complex models that better approximate real-world signals . Our interpretation makes it intuitive to inspect , compare , and alter the models since all prior knowledge is encoded in the Gaussian process kernel functions . We utilise a state space representation to perform efficient inference via Kalman smoothing , and we demonstrate how our interpretation allows for efficient parameter learning in the frequency domain .
In this paper , we propose a generalization of a region based Bayesian detection-estimation approach that addresses (i)-(ii ) simultaneously as a bilinear inverse problem . The proposed extension relies on a 2-class Gamma-Gaussian prior mixture modeling to classify the voxels of the brain region either as activated or unactivated . Our approach provides both a spatial activity map and a HRF estimation using Monte Carlo Markov chain ( MCMC ) techniques .
Firstly we utilize the method of global search to obtain the quick result of the epicenter in the coarse grid condition , and then we determine the final source location just by the local search within the epicenter area with a fine grid . We also employ a 3-D spatial Gaussian filter to obtain the source location and suppress the background noise automatically when the imaging results are scattered or unfocused , getting rid of searching for the major spatial energy points in the conventional least square method . The results based on the synthetic data show that the method combining the varying-grid 3-D Gaussian beam and spatial Gaussian filter can not only ensure the high efficiency with less steps of ray tracing but also can be more adaptable to the low signal-to-noise ratio data and scattered or unfocused imaging energy .
The charge assignment and back interpolation methods are currently bottlenecks , and take up to one‐third the execution time of the multigrid method each . Here , we propose alternative approaches to both charge assignment and back interpolation where convolution is used both to map Gaussian representations of atomic charges onto the grid and to map the forces computed at grid points back to atomic positions . These approaches achieve the same force accuracy with reduced run time .
We give necessary and sufficient conditions for a Gaussian quantum channel to have a dilation involving a passive , i.e. , number-preserving unitary . We then establish a normal form of such channels : any passively dilatable channel is the result of applying passive unitaries to the input and output of a Gaussian additive channel .
To improve the spatial resolution of video , a superresolution reconstruction method based on a sliding window is proposed utilizing the movement information between frames in the low-resolution video . We propose a registration algorithm based on a four-parameter transformation model through Taylor series expansion , using an iterative solving method as well as the Gaussian pyramid image model to estimate the movement parameters from coarseness to fine . Superresolution frames are reconstructed using an iterative back projection ( IBP ) algorithm .
The performance of the proposed models is evaluated in various actual cases . The results obtained indicate that the CI-Gaussian model possesses a higher performance in the prediction of the main caving span in actual cases when compared to the other models . These results confirm that it is not possible to consider all the effective parameters in an empirical relationship due to a higher error in the prediction .
Invited Lectures.- Grobner Bases : A Short Introduction for Systems Theorists.- Design for Reuse via Structuring Techniques for ASMs.- Computer Aided Systems Theory.- On CAST.FSM Computation of Hierarchical Multi-layer Networks of Automata.- A Holarchical Organized Design Assistant for Hierarchical Decompositions.- Conflict Management in an Intelligent Multiagent Robotics System Based on FSM Approach.- Computer-Aided Simulations of Gaussian Processes and Related Asymptotic Properties.- Hybrid Systems ' Properties - Classification and Relation to Computer Science.- Assertion-Based Analysis of Hybrid Systems with PVS.- Algebraic Description of Physical Systems.- Information Structure and Logical Design of Social Rules.- Towards a Generic Model of Information Processing Systems.- Evolutionary Information and Decision Support Systems : An Integration Based on Ontologies.- A Proposal for Project Management Using CommonKADS.- A General Planning Method for Allocation of Human Resource Groups.- Optimizing Organizational Structure :
In the case of binary MNIST , the data points are binarized by setting pixel values larger than 0.5 to 1 , and values smaller than 0.5 to 0 . subsubsection : PixelGAN Autoencoders with Gaussian Prior on MNIST Here we describe the model architecture used for training the PixelGAN autoencoder with a Gaussian prior on the binary MNIST dataset in : mnist ] Figure [ reference ]
To endow the network with selective attention without sacrificing the benefits of gradient descent training , we take inspiration from the differentiable attention mechanisms recently used in handwriting synthesis and Neural Turing Machines . Unlike the aforementioned works , we consider an explicitly two - dimensional form of attention , where an array of 2D Gaussian filters is applied to the image , yielding an image ‘ patch ’ of smoothly varying location and zoom . This configuration , which we refer to simply as “ DRAW ” , somewhat resembles the affine transformations used in computer graphics - based autoencoders .
For all our experiments on TIMIT , we pretrain the neural network with a Deep Belief Network . Since the inputs are real - valued , the first layer was pre - trained as a Gaussian RBM . Visible biases were initialized to zero and weights to random numbers sampled from a zero - mean normal distribution with standard deviation 0.01 .
Next we cap strong gradients using a sigmoid ↦x -/ 2 (+ 1exp (- ⁢ςx )) 1 , to help canceling out effects of varying illumination . We smooth gradients one more time for each orientation with a Gaussian filter of radius ν3 . Finally , the descriptor for each pixel is obtained by the ℓ2 - normalized concatenation of 8 oriented gradients and a ninth small constant value μ .
section : Message Passing In the dense CRF formulation , message passing is implemented by applying M Gaussian filters on Q values . Gaussian filter coefficients are derived based on image features such as the pixel locations and RGB values , which reflect how strongly a pixel is related to other pixels .
According to the joint representation , we then generate the parameters and , which parameterise the variational distribution over the question semantics . To emphasise , though both and are modelled as parameterised Gaussian distributions , as an approximation only functions during inference by producing samples to compute the stochastic gradients , while is the generative distribution that generates the samples for predicting the question - answer relatedness . Based on the samples , we use SGVB to optimise the lower bound ( Eq .
Previous approaches describe data with simple summary statistics or kernel function , and then use statistical tests to determine the difference . However , a summary statistics-based approach can not capture complicated underlying structure , and a kernel function-based approach lacks interpretability of results . RESULTS We propose a novel method D(3)M , for detection of differential distribution of methylation , based on distribution-valued data .
CMSM ( Constraint Mutual Subspace Method ) constructs a class subspace for each person and makes the relation between class subspaces by projecting them onto a generalized difference subspace so that the canonical angles between subspaces are enlarged to approach to the orthogonal relation . GDA ( Generalized Discriminant Analysis ) adopts kernel function operator to make it easy to extend and generalize the classical Linear Discriminant Analysis to a non linear one . Both CMSM and GDA are effective to recognize human faces , however , CMSM constructs a subspace from several face images and GDA needs only one face image to perform recognition .
The classification principle of SVM is discussed , and three kinds of kernel function are applied to SVM , which are multinomial function , radial basis function , perceptron function . Finally comparison of three kernel function characteristic is presented by three recognition examples : classification of point set in plane , recognition of similar script Chinese characters , and recognition of double screw curve . The rules of kernel function selection in different recognition matter are presented .
The algorithm called evolutionary KPCA-LSSVM is the combination of genetic algorithm ( GA ) , kernel principal component analysis ( KPCA ) and Least Squares Support Vector Machine ( LSSVM ) , which can obtain better fault recognition rate . Firstly , kernel function concept is introduced , and then GA is used to select the kernel parameter in order to improve the performances of nonlinear feature extraction and fault classification of KPCA-LSSVM method . Secondly , KPCA is used to extract the nonlinear principal features of loopers by adopting the optimal kernel trick to map nonlinearly the data into a feature space and employing the PCA procedure .
This allows one to use fast optimized Poisson solvers while satisfying simultaneously the divergence-free flow state and the no-slip condition at the body surface . To compute the divergence of the momentum equation ( in the PISO loop ) and the interpolation of the fluxes , we propose an hybrid calculation with an analytical resolution ( using the kernel function equation ) of the quantities involving the force term ( singular quantities ) . A careful and original verification study has been carried out which allows to estimate three different errors related to the discretization and to the ( IBM ) .
The work that carried out in this paper is to develop a model that can predict better accuracy of churn . We used the existing models of SVM and kernel function of it and collaborated with the artificial neural networks in prediction . The results proved to be outstanding when both the models integrated with each other .
The present work proposes a review and comparison of different Kernel functionals and neighborhood geometry for Nonlocal Means ( NLM ) in the task of digital image filtering . Some different alternatives to change the classical exponential kernel function used in NLM methods are explored .
Event-Driven Emotion Cause Extraction with Corpus Construction Evolutionary kernel machines A New Extension of Kernel Principal Component Analysis for Finger Vein Authentication Isolated Spoken Odia Digit Recognition Using Support Vector Machine Learning kernel-based HMMs for dynamic sequence synthesis Regional flood frequency analysis using kernel‐based fuzzy clustering approach Displacement Prediction of Landslide Influenced by the Periodic Precipitation, Reservoir Level and Groundwater Level Fluctuations DeepWalk: online learning of social representations Deep Fried Convnets Learning a Discriminative Null Space for Person Re-identification Using LTAG Based Features in Parse Reranking SVM Based Nonparametric Model Identification and Dynamic Model Control Comparison of machine learning algorithms for soil type classification Human Detection Based on SVM and Improved Histogram of Oriented Gradients Improved Pedestrian Detection Based on Extended Histogram of Oriented Gradients Optimization of Tongue Gesture Processing Algorithm for Standalone Multimodal Tongue Drive System Application of the Evidence Procedure to Linear Problems in Signal Processing Four-scale linear model for anisotropic reflectance (FLAIR) for plant canopies. II. validation and inversion with CASI POLDER, and PARABOLA data at BOREAS Four-Scale Linear Model for Anisotropic Reflectance (FLAIR) for plant canopies. I. Model description and partial validation Kernels for below-upper-bound parameterizations of the hitting set and directed dominating set problems Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data Down Syndrome Diagnosis Based on Gabor Wavelet Transform Combinatorics, Algorithms, Probabilistic and Experimental Methodologies, First International Symposium, ESCAPE 2007, Hangzhou, China, April 7-9, 2007, Revised Selected Papers Generalized Core Vector Machines Domain-Adversarial Training of Neural Networks On gradient regularizers for MMD GANs Automatic Facial Expression Recognition by Facial Parts Location with Boosted-LBP Early fault diagnosis method for complex equipment Forest Fire Area Estimation using Support Vector Machine as an Approximator An Improved Kernel Principal Component Analysis for Large-Scale Data Set Matrix-based Kernel Principal Component analysis for large-scale data set Support Matrix Machine for Large-Scale Data Set The Support Vector Machine (SVM) Based Near-Infrared Spectrum Recognition of Leaves Infected by the Leafminers Early prostate cancer diagnosis by using artificial neural networks and support vector machines Eigen-analysis of nonlinear PCA with polynomial kernels Non-negative Compatible Kernel Construction for Face Recognition PKCS: A Polynomial Kernel Family With Compact Support for Scale- Space Image Processing An Approaching Machine Learning Model: Tile Inspection Case Study A Unified Methodology for Computing Accurate Quaternion Color Moments and Moment Invariants Similarities and differences in spatial and non-spatial cognitive maps A support vector machine classifier based on a new kernel function model for hyperspectral data Double SVMBagging: A New Double Bagging with Support Vector Machine Gait identification using fractal analysis and support vector machine Automated detection of heart ailments from 12-lead ECG using complex wavelet sub-band bi-spectrum features Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques. The Research on Breaker Fault Status Parameter Classification of Improved Particle Swarm Optimization Kernel-based Algorithms and Visualization for Interval Data Mining Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification Accurate and Diverse Sampling of Sequences Based on a "Best of Many" Sample Objective Using Latent Variable Models to Observe Academic Pathways Efficient Multi-Sensor Extended Target Tracking using GM-PHD Filter Unifying Probabilistic Models for Time-frequency Analysis Bayesian Joint Detection-Estimation of Brain Activity Using MCMC With a Gamma-Gaussian Mixture Prior Model Source location using 3-D Gaussian beam migration imaging Efficient charge assignment and back interpolation in multigrid methods for molecular dynamics On quantum additive Gaussian noise channels Video superresolution reconstruction based on subpixel registration and iterative back projection Prediction of the main caving span in longwall mining using fuzzy MCDM technique and statistical method Computer Aided Systems Theory - EUROCAST 2001 : a selection of papers from the 8th International Workshop on Computer Aided Systems Theory, Las Palmas de Gran Canaria, Spain, February 19-23, 2001 : revised papers PixelGAN Autoencoders DRAW: A Recurrent Neural Network For Image Generation Improving neural networks by preventing co-adaptation of feature detectors DeepMatching: Hierarchical Deformable Dense Matching Conditional Random Fields as Recurrent Neural Networks Neural Variational Inference for Text Processing D3M: detection of differential distributions of methylation levels Face Recognition Based on Complementary Matching of Single Image and Sequential Images Support vector machine and kernel function characteristic analysis in pattern recognition Application for fault diagnosis of loopers based on evolutionary KPCA-LSSVM An immersed boundary method in OpenFOAM : Verification and validation A Hybrid Classification Approach for Customer Churn Prediction using Supervised Learning Methods: Banking Sector On the comparison of different kernel functionals and neighborhood geometry for nonlocal means filtering
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EMNLP ACSC Pacific Conference on Computer Graphics and Applications KDD '14 2015 IEEE International Conference on Computer Vision (ICCV) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) EMNLP ICNC (1) Combinatorics, Algorithms, Probabilistic and Experimental Methodologies J. Mach. Learn. Res. NeurIPS IJCCI ISNN (2) IJCNN ICICIC (3) CCBR ICDM Workshops Comput. Environ. Urban Syst. CVPR ICASSP (5) NIPS ICML ArXiv International Journal of Computer Vision 2015 IEEE International Conference on Computer Vision (ICCV) ICML IIH-MSP
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