We describe a framework for solving regression problems by reduction to classification . Our reduction is based on symmetrization of margin-based loss functions commonly used in boosting algorithms , namely , the logistic loss and the exponential loss . Simplified Molecular Input Line Entry Specification ( SMILES ) are input to the NGN while Feature vectors ( or chemical descriptors ) are input to the ANN . We perform training and testing on a regression-type problem wherein we predict the Lethal Dose for 50 % ( LD50 ) of the population of a given organism for the molecules in each dataset . The results of the experiment indicates that the SMILES-NGN method outperformed the ANN method in QSAR . In the proposed technique , 99.78 % of room level classifications are correctly classified using K-nearest Neighbor ( KNN ) . For regression based problem , an Artificial Neural Network in Back Propagation ( ANNBP ) performs an accuracy of 50 % and 100 % for errors less than 0.5 m and 0.9 m respectively . The root-mean-square error ( RMSE ) for regression based localization is 0.56 . This prediction problem is one of a class of problems in which the relationship between the predictors and the criterion is probabilistic . The study presented explored how neural network technology would compare to regression in problem of this type . When to terminate training in the probabilistic situation is one of the primary questions addressed . The core process is cautiously designed as a regression problem , which is then best handled by grouping various learning algorithms with their massive resource of historical production datasets . The characteristics of the currently most popular learning models used in regression problem analysis are as well investigated and compared . The performance indicates our steel quality control prediction system based on ensemble machine learning model can offer promising result whilst delivering high usability for local manufacturers to address the production problem by aid of development of machine learning techniques . Abstract It has been shown in previous research that regression modeling can be used in order to predict the number of bicycles registered by a bicycle counter . To improve the prediction accuracy , it has also been suggested that a long-term trend curve estimate can be incorporated in a regression problem formulation . A long-term trend curve estimate aims to capture those factors that are difficult , or even impossible , to explicitly model as input variables in the regression model . Analysis of different grid types for selection of SVM parameters is conducted . Experimental results obtained on the basis of applying nested grids are presented , and efficiency of regression problem solution with various support vector machine parameters values is investigated . Summary . In the paper we focus on the regression problem solving by Excel . Some faults concerning Rsquare coeffi cient are presented . To illustrate this point , we discuss a number of practical but challenging problems arising from semiconductor manufacturing process in this chapter . We first show how machine learning techniques , especially those regression-related problems , often under the “ disguise ” of optimization problems , have been used frequently ( often with nontrivial modeling skills and mathematical sophistications ) to solve the semiconductor problems . We discuss such examples as process variation modeling and VLSI chip testing . The RPN is first used to generate text region proposals , which are axis - aligned bounding boxes that enclose the arbitrary - oriented texts ( Figure 1 ( b ) ) . And then for each proposal , several ROIPoolings with different pooled sizes ( 7 × 7 , 11 × 3 , 3 × 11 ) are performed on the convolutional feature maps and the pooled features are concatenated for further classification and regression . With concatenated features and fully connected layers , we predict text / non - text scores , axis - aligned boxes and inclined minimum area boxes ( Figure 1 ( c ) ) . Our problem may seem to be a classification problem with a label space . However conceptually we are predicting relation scores , which can be considered a regression problem despite that for ground - truth we can only automatically generate targets . subsection : Zero - shot Learning Recent direct approaches make use of deep learning . Regression - based approaches train an end - to - end network to predict 3D joint locations directly from the image . Li incorporate model joint dependencies in the CNN via a max - margin formalism , others impose kinematic constraints by embedding a differentiable kinematic model into the deep learning architecture . section : ℛ = ∎ section : III . JOINT MAXIMUM PURITY FOREST FOR REGRESSION AND CLASSIFICATION section : III.1 The Workflow of Joint Maximum Purity Forest Thus , there are parameters at the - th layer . Considering the last regression layer for the output unit , which has parameters , the total number of parameters for CIN is . Note that CIN is independent of the embedding dimension . and are losses for heading angle prediction while and are for box size . Softmax is used for all classification tasks and smooth - ( huber ) loss is used for all regression cases . paragraph : Corner Loss for Joint Optimization of Box Parameters The general approximated nearest neighbor ( ANN ) search framework [ reference ][ reference ] is an efficient strategy for large - scale image retrieval , which mainly consists of 4 parts : ( 1 ) extracting compact features ( e.g. , locality - sensitive Hashing ( LSH ) [ reference ] feature ) for a query image ; ( 2 ) coarse - level search using Hamming distance to measure the similarity between binary compact Hash features , then narrow the search scope into a smaller candidate group ; ( 3 ) fine - level search by using Euclidean distance to measure the similarity between their corresponding feature vectors ; and ( 4 ) finding the object in the smaller candidate group that is the nearest one to the query image . In the inference stage of conventional RF - based SISR , PCA projection is worked as a Hash - like function to compress the feature dimension for decreasing the search range , which can speed up the searching as the coarse - level search in a CBIR framework , but the impact of using PCA on feature dimensionality reduction has been overlooked in previous works [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] . Inspired by the finelevel search using augmented features in CBIR frameworks , the high dimensional features in the leaf nodes in an RF can further improve the prediction accuracy in the regression step , which has not been studied previously . Consequently , we use the original features , rather than Meanwhile , the model parameters only increase 0.2 M , making DU - Net even more parameter efficient . Besides , the regression supervision outperforms the detection one no matter in the iterative or non - iterative setting , making it a better choice for landmark localization . Further , we compare iterative DU - Net ( 4 ) with non - iterative DU - Net ( 8 ) . It provides an opportunity to compare our approach to a credible alternative that was independently tested on the same data . Like a number of aforementioned formulations , Isola et al . use a composite loss that combines a GAN and a regression term . The authors use the Cityscapes dataset and synthesize 256×256 images for given semantic layouts . Before designing new audio features , we explore the usage of traditional local features , while adding a stage of encoding with a pre-computed codebook and a stage of pooling to get compact vectorial representations . We experiment with different encoding methods , namely the LASSO , vector quantization ( VQ ) and cosine similarity ( CS ) . We evaluate the representations ' quality in two music information retrieval applications : query-by-tag and query-by-example . In particular , we used the well-known reliability ratio ( Fuller , 1987 ) for the OLS estimates and a reliability ratio-like factor according to Sørensen et al. ( 2014 ) for the naive LASSO estimates in the presence of measurement error . In summary , we found that the MSE values of both the naive LASSO and the naive OLS increase with growing measurement error variance and covariate correlation . With respect to the corrected LASSO and OLS estimates , our results suggest that there does not exist any overall evidence of the efficacy of the applied measurement error correction factors . In this paper , we formulate the problem of support recovery in the non ideal setting , i.e. , when the correlation matrix is estimated with finite L. The resulting support recovery problem which explicitly utilizes the correlation knowledge , can be formulated as a LASSO . The performance of such “ correlation aware ” LASSO is analyzed by providing lower bounds on the probability of successful recovery as a function of the number L of measurement vectors . Numerical results are also provided to demonstrate the superior performance of the proposed correlation aware framework over conventional MMV techniques under identical conditions . In this paper a novel , memory efficient and practically useful Ensemble Bagging at Meta level Framework denoted as Meta_LASH Tree is proposed which comprises of two phases . In the first phase a memory efficient base learner named as LASSO Regression Hoeffding Tree ( LASH Tree ) is constructed , which incorporates Hoeffding Tree and LASSO Regression , that produces better predictions and better insights than using both the models separately . This hybrid model is highly interpretable and can have an insights of both linear and non linear relationship of the data . Les etudes cliniques permettent de mesurer de nombreuses variables repetees dans le temps . Lorsque l'objectif est de les relier a un critere clinique d'interet , les methodes de regularisation de type LASSO , generalisees aux Generalized Estimating Equations ( GEE ) permettent de selectionner un sous-groupe de variables en tenant compte des correlations intra-patients . Les bases de donnees presentent souvent des donnees non renseignees et des problemes de mesures ce qui entraine des donnees manquantes inevitables . The terms of the model representing the PC scores were statistically significant ( p<0.01 ) . In LASSO analysis , the cross-validated accuracy improved by 25 % when the PC scores were added to the input ( compared to using the mean BPND alone ) . These results demonstrate that a ) the disease- and tracer-specific binding patterns can be identified in sub-cortical brain structures from high-resolution PET images , and b ) such patterns may facilitate better models of the clinical disease metrics . In this paper , we apply weighted graphical LASSO ( wgLASSO ) algorithm to integrate a data-driven network model with prior biological knowledge ( i.e. , protein-protein interactions ) for biological network inference . We propose a novel differentially weighted graphical LASSO ( dwgLASSO ) algorithm that builds group-specific networks and perform network-based differential gene expression analysis to select biomarker candidates by considering their topological differences between the groups . ResultsThrough simulation , we showed that wgLASSO can achieve better performance in building biologically relevant networks than purely data-driven models ( e.g. , neighbor selection , graphical LASSO ) , even when only a moderate level of information is available as prior biological knowledge . An adaptive clutter rejection filter can then be designed to remove the clutter components . According to our simulation study , the proposed LASSO EEMD approach performs better than the state-of-the-art eigen-based and EMD method in estimating the blood flow velocity . Although the LASSO EEMD derived filter only achieves slightly better results than the cubic regression derived filters at most part of the simulated blood flow center frequencies , the proposed LASSO EEMD algorithm achieves much improved performance over cubic regression at extreme cases when the blood flow center frequency is close to or much higher than that of the clutter . This paper contributes to model the industry interconnecting structure in a network context . General predictive model ( Rapach et al. 2016 ) is extended to quantile LASSO regression so as to incorporate tail risks in the construction of industry interdependency networks . Empirical results show a denser network with heterogeneous central industries in tail cases . The asymptotic normality result is obtained for local Whittle estimators of all model parameters in a general formulation of multivariate long memory . The result is then used in devising a global statistical test for the so-called fractal non-connectivity , and in deriving the asymptotics of LASSO estimators of parameters in the so-called long-run variance matrix and its inverse . Some numerical illustrations are also provided . Recently , several learning methods have been proposed to build a performance prediction model based on performance data collected from a small sample of configurations , and then use the model to predict system performance under a new configuration . In this paper , we propose a novel approach to model highly configurable software system using a deep feedforward neural network ( FNN ) combined with a sparsity regularization technique , e.g. the L1 regularization . Besides , we also design a practical search strategy for automatically tuning the network hyperparameters efficiently . Furthermore , we propose two new techniques . The first is based on a smooth ( differentiable ) convex approximation for the L1 regularizer that does not depend on any assumptions about the loss function used . The other technique is a new strategy that addresses the non-differentiability of the L1-regularizer by casting the problem as a constrained optimization problem that is then solved using a specialized gradient projection method . In this work , we focus on the application of L1 regularization to a class of inverse problems ; relaxation-relaxation , T1-T2 , and diffusion-relaxation , D-T2 , correlation experiments in NMR , which have found widespread applications in a number of areas including probing surface interactions in catalysis and characterizing fluid composition and pore structures in rocks . We introduce a robust algorithm for solving the L1 regularization problem and provide a guide to implementing it , including the choice of the amount of regularization used and the assignment of error estimates . We then show experimentally that L1 regularization has significant advantages over both the Non-Negative Least Squares ( NNLS ) algorithm and Tikhonov regularization . The experiment results demonstrate that our method needs far below the traditional Nyquist rate to guarantee successful imaging . Compared to the prevalent L1 regularization-based methods , there is a significant reduction of the sampling rate for SAR imaging . The sampling rate used by our method is about half of the L1 regularization-based methods in the real SAR data experiments . Driver gene selection is crucial to understand the heterogeneous system of cancer . To identity cancer driver genes , various statistical strategies have been proposed , especially the L1-type regularization methods have drawn a large amount of attention . However , the statistical approaches have been developed purely from algorithmic and statistical point , and the existing studies have applied the statistical approaches to genomic data analysis without consideration of biological knowledge . In GSR-NCR , the local sparsity and nonlocal self-similarity of images is simultaneously considered in a unified framework . Different from the previous methods based on sparsity-promoting convex regularization , we extend the non-convex weighted Lp ( 0 < p < 1 ) penalty function on group sparse coefficients of the data matrix , rather than conventional L1-based regularization . To reduce the computational complexity , instead of learning the dictionary with a high computational complexity from natural images , we learn the principle component analysis ( PCA ) based dictionary for each group . In this paper , we will propose two types of tolerant fuzzy c-means clustering with regularization terms . One is L2-regularization term and the other is L1-regularization one for tolerance vector . Introducing a concept of clusterwise tolerance , we have proposed tolerant fuzzy c-means clustering from the viewpoint of handling data more flexibly . This algorithm minimizes an objective function of the form φ(Bu ) + 1/2||FpSu - f||2 , where φ is the regularization term which may be nonsmooth . In image reconstruction , the φ term corresponds to total variation smoothing and/or L1 regularization term . The least square term 1/2||FpSu - f||2 is the fidelity term . Experimental results . The node feature representations are input to a one - vs - rest logistic regression classifier with L2 regularization . The train and test data is split equally over 10 random instances . We used mini - batch stochastic gradient decent and empirically found it effective to use a gradient clipping method with growing clipping values for the different tasks ; concretely , we employed the simple function : , where is the number of bi - LSTM layers involved in each task , and is the maximum value . We applied our successive regularization to our model , along with L2 - norm regularization and dropout dropout2014ver . More details are summarized in the supplemental material . paragraph : The objective function Our objective combines a cross - entropy objective for the SNLI classification task , cross - entropy objectives and for the parsing decision for each of the two sentences at each step , and an L2 regularization term on the trained parameters . The terms are weighted using the tuned hyperparameters and : subsection : Regularization For regularization , we employ two commonly used techniques : dropout and L2 weight regularization . We apply dropout to prevent co - adaptation . This approximation step is an important part of the VAT algorithm that makes it readily applicable for various settings and model architectures . Finally , the fourth advantage is not to be overlooked , because this is the most essential point at which our VAT is fundamentally different from popular regularization methods like L p regularization . For linear models , L p regularization has an effect of mitigating the oversensitivity of the output with respect to input , and one can control the strength of its effect via the hyperparameters . Here , regularization took several forms . First , in all layers , we bounded the absolute value of the kernel weights and applied both L1 and L2 regularization to prevent overfitting . This is done by adding the regularization terms to the negative log - probability ( i.e. , where and are coefficients for L1 and L2 regularization terms respectively ) . In many cases , outliers are contained in sensor signals , and these deteriorate performances of control systems , e.g. , UAV and UGV using non-contact sensors . One of reduction methods of the outliers is robust Kalman filter ( RKF ) via l1 regression . The method is easy to implement and compute due to a simple structure and convex optimization problem , so the method attracts many attentions . This paper tries to compare more accurate and efficient L1 norm regression algorithms . Other comparative studies are mentioned and their conclusions are discussed . These procedures can be generalized by introducing weights ; yielding so-called weighted Wilcoxon ( WW ) techniques . In this paper we demonstrate how WW-estimates can be calculated using an L1 regression routine . More importantly , we present a collection of functions that can be used to implement a robust analysis of a linear model based on WW-estimates . In this paper , we propose a new prediction scheme using greedy feature selection method which is expected to be effective under large scale feature in limited number of dataset . With the help of the new scheme , we could solve the overfitting problem when using $ l_{1}$ regression as well as giving satisfying prediction result . Experimental results using longitudinal pyro-sensor dataset of health score of elderly people show that our new scheme offers better interpretability as well as achieves better prediction accuracy compared with Lasso 48 , 779 - 802 ] of linear L"1 ( least absolute deviations ) regression with linear equality and inequality constraints on the parameters . Using the same active set framework of the reduced gradient algorithm ( RGA ) , we investigate the effect on the L"1 regression estimate of small perturbations to the constraints ( constants and coefficients ) . It is shown that the constrained estimate is stable , but not uniformly stable , and in certain cases it is unchanged . We then generalize our results to arbitrary graph metrics . Our approach is to recast the problem of estimating Earth-Mover distance in terms of an l1 regression problem . The resulting linear sketches also yield space-efficient data stream algorithms in the usual way . We have successfully predicted the student ’s final CGPA and their placement result . We have predicted the results using algorithms like Linear Regression ( LR1 ) , Decision tree ( DT ) algorithm , K-nearest neighbor algorithm ( KNN ) , Logistical regression ( LR2 ) and Lasso regression ( LR3 ) algorithm . And we have obtained excellent results with accuracy over 94 % . To our knowledge , this is the first demonstration of GANs successfully generating “ labels ” , which are nearly discrete , rather than “ images ” , with their continuous - valued variation . Although cGANs achieve some success , they are far from the best available method for solving this problem : simply using L1 regression gets better scores than using a cGAN , as shown in Table [ reference ] . We argue that for vision problems , the goal ( i.e. predicting output close to the ground truth ) may be less ambiguous than graphics tasks , and reconstruction losses like L1 are mostly sufficient . In three-dimensional space , the traditional algorithm of virtual force applies to continuous target tracking process is prone to problems of distraction and shorter time in keeping tracking of the target . Aiming at solving these problems , this paper proposed a suitable three-dimensional space deployment algorithm apply to continuous target tracking . The virtual force in this algorithm is combined with the inter-node force , obstacle repulsion , attractions generated by monitored-path and tracking target . The objective of this study is to investigate how the changes of task requirement of a visual−motor task are reflected by the changes of pupil size . In the present study , a simple continuous aiming task is performed and the task requirement is manipulated and measured by Fitts ’ Index of Difficulty ( ID ) , calculated for different combinations of the target size and movement distance . Pupil response is recorded using a remote eye-tracker . Based on the environmental conditions of the Zhanjiang sea area in Guangdong Province , this paper designs the ocean sonar signal waveforms , analyzes the sonar signals with appropriate methods , and constructs an ocean sonar signal processing platform . The research results show that in the marine environment , multi-base sonar system can realize continuous transmitting of sonar signals and receive echoes to achieve continuous detection of targets . Continuous sonar signals have a lower signal-to-noise ratio ( SNR ) than pulse sonar signals , but continuous sonar signals have a longer time bandwidth product ( TBP ) , which reduces the negative impact of the ocean environment . This hammer system uses only the first mode of vibration for a desired hitting . The angular acceleration for a continuous hitting can be obtained from numerical calculations . However , this method is not useful for on-line processing Therefore , the multi-layered neural networks are applied to the calculation of the parameters of the angular acceleration . We therefore present a framework for the holistic design of ORCs enabling the simultaneous optimization of the process and the working fluid based on process performance . The simultaneous optimization is achieved by exploiting the rich molecular picture underlying the PC-SAFT equation of state in a continuous-molecular targeting approach ( CoMT-CAMD ) . To allow for the prediction of caloric properties , a q ... Due to the complex task of destination choice modelling , there are just few agent-based simulations available . This paper presents a continuous target-based simulation that simulates long-distance travel behavior for a long period of time . It is shown how destination choice and mode choice is modelled in this agent-based simulation . Moreover , the pipeline filtering is introduced to achieve the target confirmation by multi-frame judgment , reduce false alarm rate and complete the moving target detection for the infrared image sequence . Experimental results show that the proposed algorithm can achieve continuous target detection and have a higher detection precision via real long wave infrared image sequences . For continuous target following under complex scene , an objective following calculation in light of multi-shading joint likelihood investigation model was introduced . The calculation embraced shading histogram to speak to the actual factual trademark with Camshaft standard and completed exploratory research in such angles as multichannel joint shading highlights measurements , projection delineate weighted preparing , the following window size and position ascertaining , calculation handling component of course . Primates demonstrate an outstanding ability of gaining a continuous tracking of target in cluttered environments after target disappears in the scene for a long time while it is still a challenge for artificial visual system to do so . Research in psychology indicates that selective visual attention with two attention selection processes is crucial to visual tracking . In image-guided interventions , accurate knowledge about spatial relations is essential . We present several new concepts for advanced hybrid tracking systems : first , mathematical modelling of an instrument 's flexible parts , amplified by computer vision methods ; then , hybrid image-based and electromagnetic ( EM ) tracking with a a continuous output model ; finally , a new tracking paradigm , by robotically keeping the EM tracking sensor always close to the centre of the tracking volume . Neurofuzzy systems take advantage of fuzzy logic and of the learning and approximation capabilities of neural networks to build fuzzy rule bases , while preserving the massively parallel computing character . Fuzzy decision trees provide a way to manipulate fuzzy information and continuous input/output models while maintaining the interpretability and effectiveness of classical decision trees . A combination between the two ideas is discussed , which performs a global gradient-descent type optimization of the fuzzy rules corresponding to a tree , yielding improved accuracy while maintaining interpretability . We observe many continuous output problems in computer vision are naturally contained in closed geometrical manifolds , like the Euler angles in viewpoint estimation or the normals in surface normal estimation . A natural framework for posing such continuous output problems are n-spheres , which are naturally closed geometric manifolds defined in the R^(n+1 ) space . By introducing a spherical exponential mapping on n-spheres at the regression output , we obtain well-behaved gradients , leading to stable training . Analog optical neural networks permit us to assemble continuous - input , continuous-output systems . These can have great advantages relative to their digital counterparts . Navigated bronchoscopy provides benefits for endoscopists and patients , but accurate tracking information is needed . We present a novel real-time approach for bronchoscope tracking combining electromagnetic ( EM ) tracking , airway segmentation , and a continuous model of output . We augment a previously published approach by including segmentation information in the tracking optimization instead of image similarity . Nevertheless , it produces a discrete output value and this might be a drawback in certain situations . We propose in this work two approaches in order to obtain a continuous output network such as the output can be interpreted as the probability of a given pattern to belong to one of the output classes . The CC-Mantec approach utilizes a committee strategy and the results obtained both with the XOR Boolean function and with a set of benchmark functions shows the suitability of the approach , as an improvement over the standard C-Mantec algorithm is obtained in almost all cases . Then design principles and implementation schemes were put forward , and the schematics of the hardware circuits as well as the software flow chart were given in detail . The experimental results indicate that the SOPC design has the advantages of flexible controls , continuous output phase , high precision , high reliability , and that it has better application value compared with the method by using the DSP chip under the integrated development environment CCS ( code composer studio ) . The observer design is carried out through two steps . First , a high-gain observer is proposed in the continuous-time output case under the assumption that an adequate persistent excitation condition is satisfied by each subsystem . Then , the proposed observer is redesigned to handle the case of sampled outputs leading thereby to a continuous-discrete time observer .
We describe a framework for solving regression problems by reduction to classification . Our reduction is based on symmetrization of margin-based loss functions commonly used in boosting algorithms , namely , the logistic loss and the exponential loss .
Simplified Molecular Input Line Entry Specification ( SMILES ) are input to the NGN while Feature vectors ( or chemical descriptors ) are input to the ANN . We perform training and testing on a regression-type problem wherein we predict the Lethal Dose for 50 % ( LD50 ) of the population of a given organism for the molecules in each dataset . The results of the experiment indicates that the SMILES-NGN method outperformed the ANN method in QSAR .
In the proposed technique , 99.78 % of room level classifications are correctly classified using K-nearest Neighbor ( KNN ) . For regression based problem , an Artificial Neural Network in Back Propagation ( ANNBP ) performs an accuracy of 50 % and 100 % for errors less than 0.5 m and 0.9 m respectively . The root-mean-square error ( RMSE ) for regression based localization is 0.56 .
This prediction problem is one of a class of problems in which the relationship between the predictors and the criterion is probabilistic . The study presented explored how neural network technology would compare to regression in problem of this type . When to terminate training in the probabilistic situation is one of the primary questions addressed .
The core process is cautiously designed as a regression problem , which is then best handled by grouping various learning algorithms with their massive resource of historical production datasets . The characteristics of the currently most popular learning models used in regression problem analysis are as well investigated and compared . The performance indicates our steel quality control prediction system based on ensemble machine learning model can offer promising result whilst delivering high usability for local manufacturers to address the production problem by aid of development of machine learning techniques .
Abstract It has been shown in previous research that regression modeling can be used in order to predict the number of bicycles registered by a bicycle counter . To improve the prediction accuracy , it has also been suggested that a long-term trend curve estimate can be incorporated in a regression problem formulation . A long-term trend curve estimate aims to capture those factors that are difficult , or even impossible , to explicitly model as input variables in the regression model .
Analysis of different grid types for selection of SVM parameters is conducted . Experimental results obtained on the basis of applying nested grids are presented , and efficiency of regression problem solution with various support vector machine parameters values is investigated .
Summary . In the paper we focus on the regression problem solving by Excel . Some faults concerning Rsquare coeffi cient are presented .
To illustrate this point , we discuss a number of practical but challenging problems arising from semiconductor manufacturing process in this chapter . We first show how machine learning techniques , especially those regression-related problems , often under the “ disguise ” of optimization problems , have been used frequently ( often with nontrivial modeling skills and mathematical sophistications ) to solve the semiconductor problems . We discuss such examples as process variation modeling and VLSI chip testing .
The RPN is first used to generate text region proposals , which are axis - aligned bounding boxes that enclose the arbitrary - oriented texts ( Figure 1 ( b ) ) . And then for each proposal , several ROIPoolings with different pooled sizes ( 7 × 7 , 11 × 3 , 3 × 11 ) are performed on the convolutional feature maps and the pooled features are concatenated for further classification and regression . With concatenated features and fully connected layers , we predict text / non - text scores , axis - aligned boxes and inclined minimum area boxes ( Figure 1 ( c ) ) .
Our problem may seem to be a classification problem with a label space . However conceptually we are predicting relation scores , which can be considered a regression problem despite that for ground - truth we can only automatically generate targets . subsection : Zero - shot Learning
Recent direct approaches make use of deep learning . Regression - based approaches train an end - to - end network to predict 3D joint locations directly from the image . Li incorporate model joint dependencies in the CNN via a max - margin formalism , others impose kinematic constraints by embedding a differentiable kinematic model into the deep learning architecture .
section : ℛ = ∎ section : III . JOINT MAXIMUM PURITY FOREST FOR REGRESSION AND CLASSIFICATION section : III.1 The Workflow of Joint Maximum Purity Forest
Thus , there are parameters at the - th layer . Considering the last regression layer for the output unit , which has parameters , the total number of parameters for CIN is . Note that CIN is independent of the embedding dimension .
and are losses for heading angle prediction while and are for box size . Softmax is used for all classification tasks and smooth - ( huber ) loss is used for all regression cases . paragraph : Corner Loss for Joint Optimization of Box Parameters
The general approximated nearest neighbor ( ANN ) search framework [ reference ][ reference ] is an efficient strategy for large - scale image retrieval , which mainly consists of 4 parts : ( 1 ) extracting compact features ( e.g. , locality - sensitive Hashing ( LSH ) [ reference ] feature ) for a query image ; ( 2 ) coarse - level search using Hamming distance to measure the similarity between binary compact Hash features , then narrow the search scope into a smaller candidate group ; ( 3 ) fine - level search by using Euclidean distance to measure the similarity between their corresponding feature vectors ; and ( 4 ) finding the object in the smaller candidate group that is the nearest one to the query image . In the inference stage of conventional RF - based SISR , PCA projection is worked as a Hash - like function to compress the feature dimension for decreasing the search range , which can speed up the searching as the coarse - level search in a CBIR framework , but the impact of using PCA on feature dimensionality reduction has been overlooked in previous works [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] . Inspired by the finelevel search using augmented features in CBIR frameworks , the high dimensional features in the leaf nodes in an RF can further improve the prediction accuracy in the regression step , which has not been studied previously . Consequently , we use the original features , rather than
Meanwhile , the model parameters only increase 0.2 M , making DU - Net even more parameter efficient . Besides , the regression supervision outperforms the detection one no matter in the iterative or non - iterative setting , making it a better choice for landmark localization . Further , we compare iterative DU - Net ( 4 ) with non - iterative DU - Net ( 8 ) .
It provides an opportunity to compare our approach to a credible alternative that was independently tested on the same data . Like a number of aforementioned formulations , Isola et al . use a composite loss that combines a GAN and a regression term . The authors use the Cityscapes dataset and synthesize 256×256 images for given semantic layouts .
Before designing new audio features , we explore the usage of traditional local features , while adding a stage of encoding with a pre-computed codebook and a stage of pooling to get compact vectorial representations . We experiment with different encoding methods , namely the LASSO , vector quantization ( VQ ) and cosine similarity ( CS ) . We evaluate the representations ' quality in two music information retrieval applications : query-by-tag and query-by-example .
In particular , we used the well-known reliability ratio ( Fuller , 1987 ) for the OLS estimates and a reliability ratio-like factor according to Sørensen et al. ( 2014 ) for the naive LASSO estimates in the presence of measurement error . In summary , we found that the MSE values of both the naive LASSO and the naive OLS increase with growing measurement error variance and covariate correlation . With respect to the corrected LASSO and OLS estimates , our results suggest that there does not exist any overall evidence of the efficacy of the applied measurement error correction factors .
In this paper , we formulate the problem of support recovery in the non ideal setting , i.e. , when the correlation matrix is estimated with finite L. The resulting support recovery problem which explicitly utilizes the correlation knowledge , can be formulated as a LASSO . The performance of such “ correlation aware ” LASSO is analyzed by providing lower bounds on the probability of successful recovery as a function of the number L of measurement vectors . Numerical results are also provided to demonstrate the superior performance of the proposed correlation aware framework over conventional MMV techniques under identical conditions .
In this paper a novel , memory efficient and practically useful Ensemble Bagging at Meta level Framework denoted as Meta_LASH Tree is proposed which comprises of two phases . In the first phase a memory efficient base learner named as LASSO Regression Hoeffding Tree ( LASH Tree ) is constructed , which incorporates Hoeffding Tree and LASSO Regression , that produces better predictions and better insights than using both the models separately . This hybrid model is highly interpretable and can have an insights of both linear and non linear relationship of the data .
Les etudes cliniques permettent de mesurer de nombreuses variables repetees dans le temps . Lorsque l'objectif est de les relier a un critere clinique d'interet , les methodes de regularisation de type LASSO , generalisees aux Generalized Estimating Equations ( GEE ) permettent de selectionner un sous-groupe de variables en tenant compte des correlations intra-patients . Les bases de donnees presentent souvent des donnees non renseignees et des problemes de mesures ce qui entraine des donnees manquantes inevitables .
The terms of the model representing the PC scores were statistically significant ( p<0.01 ) . In LASSO analysis , the cross-validated accuracy improved by 25 % when the PC scores were added to the input ( compared to using the mean BPND alone ) . These results demonstrate that a ) the disease- and tracer-specific binding patterns can be identified in sub-cortical brain structures from high-resolution PET images , and b ) such patterns may facilitate better models of the clinical disease metrics .
In this paper , we apply weighted graphical LASSO ( wgLASSO ) algorithm to integrate a data-driven network model with prior biological knowledge ( i.e. , protein-protein interactions ) for biological network inference . We propose a novel differentially weighted graphical LASSO ( dwgLASSO ) algorithm that builds group-specific networks and perform network-based differential gene expression analysis to select biomarker candidates by considering their topological differences between the groups . ResultsThrough simulation , we showed that wgLASSO can achieve better performance in building biologically relevant networks than purely data-driven models ( e.g. , neighbor selection , graphical LASSO ) , even when only a moderate level of information is available as prior biological knowledge .
An adaptive clutter rejection filter can then be designed to remove the clutter components . According to our simulation study , the proposed LASSO EEMD approach performs better than the state-of-the-art eigen-based and EMD method in estimating the blood flow velocity . Although the LASSO EEMD derived filter only achieves slightly better results than the cubic regression derived filters at most part of the simulated blood flow center frequencies , the proposed LASSO EEMD algorithm achieves much improved performance over cubic regression at extreme cases when the blood flow center frequency is close to or much higher than that of the clutter .
This paper contributes to model the industry interconnecting structure in a network context . General predictive model ( Rapach et al. 2016 ) is extended to quantile LASSO regression so as to incorporate tail risks in the construction of industry interdependency networks . Empirical results show a denser network with heterogeneous central industries in tail cases .
The asymptotic normality result is obtained for local Whittle estimators of all model parameters in a general formulation of multivariate long memory . The result is then used in devising a global statistical test for the so-called fractal non-connectivity , and in deriving the asymptotics of LASSO estimators of parameters in the so-called long-run variance matrix and its inverse . Some numerical illustrations are also provided .
Recently , several learning methods have been proposed to build a performance prediction model based on performance data collected from a small sample of configurations , and then use the model to predict system performance under a new configuration . In this paper , we propose a novel approach to model highly configurable software system using a deep feedforward neural network ( FNN ) combined with a sparsity regularization technique , e.g. the L1 regularization . Besides , we also design a practical search strategy for automatically tuning the network hyperparameters efficiently .
Furthermore , we propose two new techniques . The first is based on a smooth ( differentiable ) convex approximation for the L1 regularizer that does not depend on any assumptions about the loss function used . The other technique is a new strategy that addresses the non-differentiability of the L1-regularizer by casting the problem as a constrained optimization problem that is then solved using a specialized gradient projection method .
In this work , we focus on the application of L1 regularization to a class of inverse problems ; relaxation-relaxation , T1-T2 , and diffusion-relaxation , D-T2 , correlation experiments in NMR , which have found widespread applications in a number of areas including probing surface interactions in catalysis and characterizing fluid composition and pore structures in rocks . We introduce a robust algorithm for solving the L1 regularization problem and provide a guide to implementing it , including the choice of the amount of regularization used and the assignment of error estimates . We then show experimentally that L1 regularization has significant advantages over both the Non-Negative Least Squares ( NNLS ) algorithm and Tikhonov regularization .
The experiment results demonstrate that our method needs far below the traditional Nyquist rate to guarantee successful imaging . Compared to the prevalent L1 regularization-based methods , there is a significant reduction of the sampling rate for SAR imaging . The sampling rate used by our method is about half of the L1 regularization-based methods in the real SAR data experiments .
Driver gene selection is crucial to understand the heterogeneous system of cancer . To identity cancer driver genes , various statistical strategies have been proposed , especially the L1-type regularization methods have drawn a large amount of attention . However , the statistical approaches have been developed purely from algorithmic and statistical point , and the existing studies have applied the statistical approaches to genomic data analysis without consideration of biological knowledge .
In GSR-NCR , the local sparsity and nonlocal self-similarity of images is simultaneously considered in a unified framework . Different from the previous methods based on sparsity-promoting convex regularization , we extend the non-convex weighted Lp ( 0 < p < 1 ) penalty function on group sparse coefficients of the data matrix , rather than conventional L1-based regularization . To reduce the computational complexity , instead of learning the dictionary with a high computational complexity from natural images , we learn the principle component analysis ( PCA ) based dictionary for each group .
In this paper , we will propose two types of tolerant fuzzy c-means clustering with regularization terms . One is L2-regularization term and the other is L1-regularization one for tolerance vector . Introducing a concept of clusterwise tolerance , we have proposed tolerant fuzzy c-means clustering from the viewpoint of handling data more flexibly .
This algorithm minimizes an objective function of the form φ(Bu ) + 1/2||FpSu - f||2 , where φ is the regularization term which may be nonsmooth . In image reconstruction , the φ term corresponds to total variation smoothing and/or L1 regularization term . The least square term 1/2||FpSu - f||2 is the fidelity term .
Experimental results . The node feature representations are input to a one - vs - rest logistic regression classifier with L2 regularization . The train and test data is split equally over 10 random instances .
We used mini - batch stochastic gradient decent and empirically found it effective to use a gradient clipping method with growing clipping values for the different tasks ; concretely , we employed the simple function : , where is the number of bi - LSTM layers involved in each task , and is the maximum value . We applied our successive regularization to our model , along with L2 - norm regularization and dropout dropout2014ver . More details are summarized in the supplemental material .
paragraph : The objective function Our objective combines a cross - entropy objective for the SNLI classification task , cross - entropy objectives and for the parsing decision for each of the two sentences at each step , and an L2 regularization term on the trained parameters . The terms are weighted using the tuned hyperparameters and :
subsection : Regularization For regularization , we employ two commonly used techniques : dropout and L2 weight regularization . We apply dropout to prevent co - adaptation .
This approximation step is an important part of the VAT algorithm that makes it readily applicable for various settings and model architectures . Finally , the fourth advantage is not to be overlooked , because this is the most essential point at which our VAT is fundamentally different from popular regularization methods like L p regularization . For linear models , L p regularization has an effect of mitigating the oversensitivity of the output with respect to input , and one can control the strength of its effect via the hyperparameters .
Here , regularization took several forms . First , in all layers , we bounded the absolute value of the kernel weights and applied both L1 and L2 regularization to prevent overfitting . This is done by adding the regularization terms to the negative log - probability ( i.e. , where and are coefficients for L1 and L2 regularization terms respectively ) .
In many cases , outliers are contained in sensor signals , and these deteriorate performances of control systems , e.g. , UAV and UGV using non-contact sensors . One of reduction methods of the outliers is robust Kalman filter ( RKF ) via l1 regression . The method is easy to implement and compute due to a simple structure and convex optimization problem , so the method attracts many attentions .
This paper tries to compare more accurate and efficient L1 norm regression algorithms . Other comparative studies are mentioned and their conclusions are discussed .
These procedures can be generalized by introducing weights ; yielding so-called weighted Wilcoxon ( WW ) techniques . In this paper we demonstrate how WW-estimates can be calculated using an L1 regression routine . More importantly , we present a collection of functions that can be used to implement a robust analysis of a linear model based on WW-estimates .
In this paper , we propose a new prediction scheme using greedy feature selection method which is expected to be effective under large scale feature in limited number of dataset . With the help of the new scheme , we could solve the overfitting problem when using $ l_{1}$ regression as well as giving satisfying prediction result . Experimental results using longitudinal pyro-sensor dataset of health score of elderly people show that our new scheme offers better interpretability as well as achieves better prediction accuracy compared with Lasso
48 , 779 - 802 ] of linear L"1 ( least absolute deviations ) regression with linear equality and inequality constraints on the parameters . Using the same active set framework of the reduced gradient algorithm ( RGA ) , we investigate the effect on the L"1 regression estimate of small perturbations to the constraints ( constants and coefficients ) . It is shown that the constrained estimate is stable , but not uniformly stable , and in certain cases it is unchanged .
We then generalize our results to arbitrary graph metrics . Our approach is to recast the problem of estimating Earth-Mover distance in terms of an l1 regression problem . The resulting linear sketches also yield space-efficient data stream algorithms in the usual way .
We have successfully predicted the student ’s final CGPA and their placement result . We have predicted the results using algorithms like Linear Regression ( LR1 ) , Decision tree ( DT ) algorithm , K-nearest neighbor algorithm ( KNN ) , Logistical regression ( LR2 ) and Lasso regression ( LR3 ) algorithm . And we have obtained excellent results with accuracy over 94 % .
To our knowledge , this is the first demonstration of GANs successfully generating “ labels ” , which are nearly discrete , rather than “ images ” , with their continuous - valued variation . Although cGANs achieve some success , they are far from the best available method for solving this problem : simply using L1 regression gets better scores than using a cGAN , as shown in Table [ reference ] . We argue that for vision problems , the goal ( i.e. predicting output close to the ground truth ) may be less ambiguous than graphics tasks , and reconstruction losses like L1 are mostly sufficient .
In three-dimensional space , the traditional algorithm of virtual force applies to continuous target tracking process is prone to problems of distraction and shorter time in keeping tracking of the target . Aiming at solving these problems , this paper proposed a suitable three-dimensional space deployment algorithm apply to continuous target tracking . The virtual force in this algorithm is combined with the inter-node force , obstacle repulsion , attractions generated by monitored-path and tracking target .
The objective of this study is to investigate how the changes of task requirement of a visual−motor task are reflected by the changes of pupil size . In the present study , a simple continuous aiming task is performed and the task requirement is manipulated and measured by Fitts ’ Index of Difficulty ( ID ) , calculated for different combinations of the target size and movement distance . Pupil response is recorded using a remote eye-tracker .
Based on the environmental conditions of the Zhanjiang sea area in Guangdong Province , this paper designs the ocean sonar signal waveforms , analyzes the sonar signals with appropriate methods , and constructs an ocean sonar signal processing platform . The research results show that in the marine environment , multi-base sonar system can realize continuous transmitting of sonar signals and receive echoes to achieve continuous detection of targets . Continuous sonar signals have a lower signal-to-noise ratio ( SNR ) than pulse sonar signals , but continuous sonar signals have a longer time bandwidth product ( TBP ) , which reduces the negative impact of the ocean environment .
This hammer system uses only the first mode of vibration for a desired hitting . The angular acceleration for a continuous hitting can be obtained from numerical calculations . However , this method is not useful for on-line processing Therefore , the multi-layered neural networks are applied to the calculation of the parameters of the angular acceleration .
We therefore present a framework for the holistic design of ORCs enabling the simultaneous optimization of the process and the working fluid based on process performance . The simultaneous optimization is achieved by exploiting the rich molecular picture underlying the PC-SAFT equation of state in a continuous-molecular targeting approach ( CoMT-CAMD ) . To allow for the prediction of caloric properties , a q ...
Due to the complex task of destination choice modelling , there are just few agent-based simulations available . This paper presents a continuous target-based simulation that simulates long-distance travel behavior for a long period of time . It is shown how destination choice and mode choice is modelled in this agent-based simulation .
Moreover , the pipeline filtering is introduced to achieve the target confirmation by multi-frame judgment , reduce false alarm rate and complete the moving target detection for the infrared image sequence . Experimental results show that the proposed algorithm can achieve continuous target detection and have a higher detection precision via real long wave infrared image sequences .
For continuous target following under complex scene , an objective following calculation in light of multi-shading joint likelihood investigation model was introduced . The calculation embraced shading histogram to speak to the actual factual trademark with Camshaft standard and completed exploratory research in such angles as multichannel joint shading highlights measurements , projection delineate weighted preparing , the following window size and position ascertaining , calculation handling component of course .
Primates demonstrate an outstanding ability of gaining a continuous tracking of target in cluttered environments after target disappears in the scene for a long time while it is still a challenge for artificial visual system to do so . Research in psychology indicates that selective visual attention with two attention selection processes is crucial to visual tracking .
In image-guided interventions , accurate knowledge about spatial relations is essential . We present several new concepts for advanced hybrid tracking systems : first , mathematical modelling of an instrument 's flexible parts , amplified by computer vision methods ; then , hybrid image-based and electromagnetic ( EM ) tracking with a a continuous output model ; finally , a new tracking paradigm , by robotically keeping the EM tracking sensor always close to the centre of the tracking volume .
Neurofuzzy systems take advantage of fuzzy logic and of the learning and approximation capabilities of neural networks to build fuzzy rule bases , while preserving the massively parallel computing character . Fuzzy decision trees provide a way to manipulate fuzzy information and continuous input/output models while maintaining the interpretability and effectiveness of classical decision trees . A combination between the two ideas is discussed , which performs a global gradient-descent type optimization of the fuzzy rules corresponding to a tree , yielding improved accuracy while maintaining interpretability .
We observe many continuous output problems in computer vision are naturally contained in closed geometrical manifolds , like the Euler angles in viewpoint estimation or the normals in surface normal estimation . A natural framework for posing such continuous output problems are n-spheres , which are naturally closed geometric manifolds defined in the R^(n+1 ) space . By introducing a spherical exponential mapping on n-spheres at the regression output , we obtain well-behaved gradients , leading to stable training .
Analog optical neural networks permit us to assemble continuous - input , continuous-output systems . These can have great advantages relative to their digital counterparts .
Navigated bronchoscopy provides benefits for endoscopists and patients , but accurate tracking information is needed . We present a novel real-time approach for bronchoscope tracking combining electromagnetic ( EM ) tracking , airway segmentation , and a continuous model of output . We augment a previously published approach by including segmentation information in the tracking optimization instead of image similarity .
Nevertheless , it produces a discrete output value and this might be a drawback in certain situations . We propose in this work two approaches in order to obtain a continuous output network such as the output can be interpreted as the probability of a given pattern to belong to one of the output classes . The CC-Mantec approach utilizes a committee strategy and the results obtained both with the XOR Boolean function and with a set of benchmark functions shows the suitability of the approach , as an improvement over the standard C-Mantec algorithm is obtained in almost all cases .
Then design principles and implementation schemes were put forward , and the schematics of the hardware circuits as well as the software flow chart were given in detail . The experimental results indicate that the SOPC design has the advantages of flexible controls , continuous output phase , high precision , high reliability , and that it has better application value compared with the method by using the DSP chip under the integrated development environment CCS ( code composer studio ) .
The observer design is carried out through two steps . First , a high-gain observer is proposed in the continuous-time output case under the assumption that an adequate persistent excitation condition is satisfied by each subsystem . Then , the proposed observer is redesigned to handle the case of sampled outputs leading thereby to a continuous-discrete time observer .
Smooth ε-insensitive regression by loss symmetrization Neural grammar networks for toxicology Indoor localization using K-nearest neighbor and artificial neural network back propagation algorithms Using neural network technology for predicting military attrition Ensemble Machine Learning Systems for the Estimation of Steel Quality Control Regression-based evaluation of bicycle flow trend estimates Selection of support vector machines parameters for regression using nested grids REMARKS ON THE FAULTY REGRESSION IN EXCEL Machine Learning for VLSI Chip Testing and Semiconductor Manufacturing Process Monitoring and Improvement R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection Learning to Compare: Relation Network for Few-Shot Learning Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image Joint maximum purity forest with application to image super-resolution xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems Frustum PointNets for 3D Object Detection from RGB-D Data Image Super-resolution via Feature-augmented Random Forest Quantized Densely Connected U-Nets for Efficient Landmark Localization Photographic Image Synthesis with Cascaded Refinement Networks Codebook-Based Audio Feature Representation for Music Information Retrieval Measurement Error in LASSO - Analytical Results and a Simulation Study On application of LASSO for sparse support recovery with imperfect correlation awareness Meta_LASH Tree: Bagging at Meta Level Using LASSO Regression Hoeffding Tree for Streaming Data Contribution à la sélection de variables en présence de données longitudinales : application à des biomarqueurs issus d'imagerie médicale. (Contribution to variable selection in the presence of longitudinal data : application to biomarkers derived from medical imaging) Sub-regional pattern analysis of heterogeneous PET tracer distribution employed for disease assessment Incorporating prior biological knowledge for network-based differential gene expression analysis using differentially weighted graphical LASSO A lasso based ensemble empirical mode decomposition approach to designing adaptive clutter suppression filters Industry Interdependency Dynamics in a Network Context Asymptotic results for multivariate local Whittle estimation with applications DeepPerf: Performance Prediction for Configurable Software with Deep Sparse Neural Network Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches Obtaining sparse distributions in 2D inverse problems. SAR imaging from compressed measurements based on L1/2 regularization Interaction-Based Feature Selection for Uncovering Cancer Driver Genes Through Copy Number-Driven Expression Level Group-based sparse representation for image compressive sensing reconstruction with non-convex regularization Comparison of tolerant fuzzy c-means clustering with L2- and L1-regularization Partially parallel MR image reconstruction using sensitivity encoding node2vec: Scalable Feature Learning for Networks A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks A Fast Unified Model for Parsing and Sentence Understanding A C-LSTM Neural Network for Text Classification Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Brain tumor segmentation with Deep Neural Networks Design method of robust Kalman filter for multi output systems based on statistics Comparative Study of the L 1 Norm Regression Algorithms Rank-Based Analysis of Linear Models Using R Robust Health Score Prediction from Pyro-Sensor Activity Data based on Greedy Feature Selection Sensitivity analysis of constrained linear L Sketching Earth-Mover Distance on Graph Metrics Predictive Analysis Tool for Predicting Student Performance and Placement Performance using ML algorithms. Image-to-Image Translation with Conditional Adversarial Networks Continuous Target Tracking Algorithm in Aerial Wireless Sensor Network Pupil responses to continuous aiming movements Research on Analyzing and Processing Methods of Ocean Sonar Signals Minimum energy driving a flexible link hammer using neural networks Simultaneous Optimization of Working Fluid and Process for Organic Rankine Cycles Using PC-SAFT Destination and mode choice in an agent-based simulation of long-distance travel demand Spatio-Temporal Saliency Fusion Based Small Infrared Moving Target Detection Under Sea-Sky Background Multi-color Joint Probability Statistics Model-based Object Tracking System Robust visual tracking based on selective attention shift Advanced Hybrid Tracking and Navigation for Computer-Assisted Interventions On neurofuzzy and fuzzy decision tree approaches Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on N-Spheres Continuous Time Neural Networks Real-time motion compensation for EM bronchoscope tracking with smooth output - ex-vivo validation Committee C-Mantec: A Probabilistic Constructive Neural Network IIR Digital Filter Design Based on DSP and SOPC High gain observer for a class of nonlinear systems with coupled structure and sampled output measurements: application to a quadrotor
a36248ef66a12d26edc0d2e766402542addad31b d56a00ab888efe28a1061585dde8f7cb167f674a 414aed1044816110ba678f69755d0e22a80fb1ed d57a1908682f28f60d61c1d39282895ced046dde c2daf8097171ba157d42d72a7c96b5681eca4d85 a2253cb04732d47f6206a85714b83423dbf467e0 d27bc02289ec0e011d2b5df6b3491a02f045ed5d 0bdc3cecd72affe4b7278861880c1498317a5ec2 f709daa18be3f369160faf5119e3aadf8716de4e 56bfbd28da9636c9cae54e2a7a5047bc00338b43 91af72cba8470a537cb48d15c87c49b28e640e4d 5e7bdff8a41569a2384b3023ff9c1f7afb08c3e6 747c50c6f448f58ab4f4272889134e39d04233bf 8b0f15195fe7ea757b8e971a209e73a2f2bf75ca e26cdb253f75667eafc93636d175cb870fba09e6 e2e5ad3d835569914213aba7d80cea7b3ff882ed a15aa562d35fe2e9694b73d6ad26ff699364f710 ac788d8621fa7a5c9b98128cb71daef4c01b3052 a7b190add6595f68af56ce6938866a625846035f 033ffaa82a8516e20f695a219c31b5727ea35558 cd34e7070ae230960fa285575ea0b1f59baadaa4 1e72117fccdc79a2f4c9b4e002256f627cdceaa9 a6090eb0e311f4d40b3751b17f2b59b2c28676be 4ed1d1f6b0793c9c03690270023f2907e74a076b d7c95cbb036ff8bec86c7b60fb0ecd0f92170498 18eb0dbf6101bd98899b780cdac73ced4be30317 5de5a55e74c47055a163394b7301decf1edb071a 4a45fb2d0368bd81c73c689786a773d09c48d07d 047938746473126c993d78e30413e403975ad0cc 0345ea4253c206c2b982ca0b33896f603ef89596 d5165087e9e52570e0c0bf1ca33a1acd9a556e4d 76e59ec6a9bbcf22c711c4eeeebcafc6cd749b77 d76c1da87fb840b785271f65cf2bc188e601435d d267b7c63381f14b25a9446bcf1aedd7680b5295 c8d79c3d3d441f6457d291e8ddd25b8c3eee236e f842b23c092dbe590986ced878b71e3bb4bca3bf 591f06c05a5f836cb42d6ef132f7f5f477b13dcd a2905c626fbbcf1acc7564794e524a16ca2a66cb fb4a78480e8d47c6f0144eb31c9e72b4f7225b53 da3e77896ad0056d95e9ed75d09b7f1086936d60 c98d29ef149948b347df7fe9cbc1cf01891b0ac6 0da43b354fab68f01da5026a1b0f42e4b8be31d5 fd26dbd30f2199d71b7530622198bcc770bbba65 458c37b606ffd962e541c20753e15ef284f6f838 59d8743159d1034d95de6deffa1156c4b91e82a9 baceb4b90524cb980ae79d25f2b287f0f53c3c9f bc100f177407f70d80136dc85575292eb3506b72 7468cb69dc79be622253b906ff7fb173ddef70a6 78a7e7ab4a3e4f3ae61e4b30aa7dfe8f6d23b74c 2b150139dff20c4084044eee16d58242a9a23aba fec7a709d6b0536601f1d1d6906860a87f31fc23
["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Mathematics","Computer Science"] ["Chemistry","Medicine"] ["Computer Science"] ["Medicine","Biology","Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Geology"] ["Computer Science"] ["Chemistry"] ["Computer Science"] ["Engineering"] ["Computer Science"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science"] ["Engineering","Computer Science"] ["Engineering","Computer Science","Physics"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"]
2003 2010 2018 1991 2018 2018 2008 2008 2019 2017 2018 2017 2018 2018 2018 2019 2018 2017 2014 2015 2012 2019 2016 2016 2017 2012 2017 2019 2019 2007 2017 2011 2017 2017 2009 2012 2016 2017 2016 2015 2019 2017 2013 2005 2005 2019 2006 2013 2020 2017 2015 2015 2019 1995 2014 2017 2019 2018 2009 2013 2000 2019 1988 2012 2013 2010 2019
COLT CIBCB WOCC BigData ANT/SEIT ArXiv 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) J. Electronic Imaging KDD 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Signal Process. Image Commun. ECCV 2017 IEEE International Conference on Computer Vision (ICCV) ACSCC ICASSP CAMSAP ICSE ECML IGARSS GrC ICIP KDD EMNLP ACL ArXiv IEEE Transactions on Pattern Analysis and Machine Intelligence Medical Image Anal. ACC PerCom Workshops APPROX-RANDOM 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IROS (3) CCA/ISIC Medical Imaging: Image-Guided Procedures IWANN (1)
https://api.semanticscholar.org/a36248ef66a12d26edc0d2e766402542addad31b https://api.semanticscholar.org/d56a00ab888efe28a1061585dde8f7cb167f674a https://api.semanticscholar.org/414aed1044816110ba678f69755d0e22a80fb1ed https://api.semanticscholar.org/d57a1908682f28f60d61c1d39282895ced046dde https://api.semanticscholar.org/c2daf8097171ba157d42d72a7c96b5681eca4d85 https://api.semanticscholar.org/a2253cb04732d47f6206a85714b83423dbf467e0 https://api.semanticscholar.org/d27bc02289ec0e011d2b5df6b3491a02f045ed5d https://api.semanticscholar.org/0bdc3cecd72affe4b7278861880c1498317a5ec2 https://api.semanticscholar.org/f709daa18be3f369160faf5119e3aadf8716de4e https://www.semanticscholar.org/paper/16da4d6503e17f8597602437358461c252244bf7 https://www.semanticscholar.org/paper/bfe284e4338e62f0a61bb33398353efd687f206f https://www.semanticscholar.org/paper/4be92654f72410d20d3a6a57a5c434b619d8605e https://www.semanticscholar.org/paper/24730424236724d3f798dec02901e7a1f1c4710e https://www.semanticscholar.org/paper/3448e6a5039417dc1ae890efeca3bef5390ace7c https://www.semanticscholar.org/paper/526cf249c2760b7bdbb28f2a2a7c85851d3c2727 https://www.semanticscholar.org/paper/16051bbe3a7f7c77a952ebf76722ea655e8906ca https://www.semanticscholar.org/paper/4543052aeaf52fdb01fced9b3ccf97827582cef5 https://www.semanticscholar.org/paper/28eceb438da0b841bbd3d02684dbfa263838ed60 https://api.semanticscholar.org/56bfbd28da9636c9cae54e2a7a5047bc00338b43 https://api.semanticscholar.org/91af72cba8470a537cb48d15c87c49b28e640e4d https://api.semanticscholar.org/5e7bdff8a41569a2384b3023ff9c1f7afb08c3e6 https://api.semanticscholar.org/747c50c6f448f58ab4f4272889134e39d04233bf https://api.semanticscholar.org/8b0f15195fe7ea757b8e971a209e73a2f2bf75ca https://api.semanticscholar.org/e26cdb253f75667eafc93636d175cb870fba09e6 https://api.semanticscholar.org/e2e5ad3d835569914213aba7d80cea7b3ff882ed https://api.semanticscholar.org/a15aa562d35fe2e9694b73d6ad26ff699364f710 https://api.semanticscholar.org/ac788d8621fa7a5c9b98128cb71daef4c01b3052 https://api.semanticscholar.org/a7b190add6595f68af56ce6938866a625846035f https://api.semanticscholar.org/033ffaa82a8516e20f695a219c31b5727ea35558 https://api.semanticscholar.org/cd34e7070ae230960fa285575ea0b1f59baadaa4 https://api.semanticscholar.org/1e72117fccdc79a2f4c9b4e002256f627cdceaa9 https://api.semanticscholar.org/a6090eb0e311f4d40b3751b17f2b59b2c28676be https://api.semanticscholar.org/4ed1d1f6b0793c9c03690270023f2907e74a076b https://api.semanticscholar.org/d7c95cbb036ff8bec86c7b60fb0ecd0f92170498 https://api.semanticscholar.org/18eb0dbf6101bd98899b780cdac73ced4be30317 https://api.semanticscholar.org/5de5a55e74c47055a163394b7301decf1edb071a https://www.semanticscholar.org/paper/36ee2c8bd605afd48035d15fdc6b8c8842363376 https://www.semanticscholar.org/paper/ade0c116120b54b57a91da51235108b75c28375a https://www.semanticscholar.org/paper/36c097a225a95735271960e2b63a2cb9e98bff83 https://www.semanticscholar.org/paper/10f62af29c3fc5e2572baddca559ffbfd6be8787 https://www.semanticscholar.org/paper/4b1c6f6521da545892f3f5dc39461584d4a27ec0 https://www.semanticscholar.org/paper/441ff323c92331e655ce9ff896773fc00b55089a https://api.semanticscholar.org/4a45fb2d0368bd81c73c689786a773d09c48d07d https://api.semanticscholar.org/047938746473126c993d78e30413e403975ad0cc https://api.semanticscholar.org/0345ea4253c206c2b982ca0b33896f603ef89596 https://api.semanticscholar.org/d5165087e9e52570e0c0bf1ca33a1acd9a556e4d https://api.semanticscholar.org/76e59ec6a9bbcf22c711c4eeeebcafc6cd749b77 https://api.semanticscholar.org/d76c1da87fb840b785271f65cf2bc188e601435d https://api.semanticscholar.org/d267b7c63381f14b25a9446bcf1aedd7680b5295 https://www.semanticscholar.org/paper/8acbe90d5b852dadea7810345451a99608ee54c7 https://api.semanticscholar.org/c8d79c3d3d441f6457d291e8ddd25b8c3eee236e https://api.semanticscholar.org/f842b23c092dbe590986ced878b71e3bb4bca3bf https://api.semanticscholar.org/591f06c05a5f836cb42d6ef132f7f5f477b13dcd https://api.semanticscholar.org/a2905c626fbbcf1acc7564794e524a16ca2a66cb https://api.semanticscholar.org/fb4a78480e8d47c6f0144eb31c9e72b4f7225b53 https://api.semanticscholar.org/da3e77896ad0056d95e9ed75d09b7f1086936d60 https://api.semanticscholar.org/c98d29ef149948b347df7fe9cbc1cf01891b0ac6 https://api.semanticscholar.org/0da43b354fab68f01da5026a1b0f42e4b8be31d5 https://api.semanticscholar.org/fd26dbd30f2199d71b7530622198bcc770bbba65 https://api.semanticscholar.org/458c37b606ffd962e541c20753e15ef284f6f838 https://api.semanticscholar.org/59d8743159d1034d95de6deffa1156c4b91e82a9 https://api.semanticscholar.org/baceb4b90524cb980ae79d25f2b287f0f53c3c9f https://api.semanticscholar.org/bc100f177407f70d80136dc85575292eb3506b72 https://api.semanticscholar.org/7468cb69dc79be622253b906ff7fb173ddef70a6 https://api.semanticscholar.org/78a7e7ab4a3e4f3ae61e4b30aa7dfe8f6d23b74c https://api.semanticscholar.org/2b150139dff20c4084044eee16d58242a9a23aba https://api.semanticscholar.org/fec7a709d6b0536601f1d1d6906860a87f31fc23