The algorithm converges to a stably local optimal solution under comparatively lenient conditions . In addition , the optimization problem to be solved includes the original SVM as a special case . However , since the amount of computation increases as the dimensions of the input space increase in the proposed algorithm , this paper proposes a simplified algorithm obtained by combining the original SVM and the abridged proposed algorithm . Finally , the best-proposed models were examined using a few examples of real lost circulation cases from the field . The results of the analysis have revealed that both ANNs and SVM approaches can be of great use , with the SVM results being more promising . The application of the machine learning methods could assist drilling engineers in modifying drilling parameters to minimize the likelihood of lost circulation . There are two rows and four columns of sub - figures . The top row shows the results of KNN , and the bottom one shows results of SVM . Each column gives the result on one dataset . Bag - of - words is a very efficient way to conduct the feature engineering . SVM and Naive Bayes are constantly the classifier . The latter , deep neural models , taking advantage of neural networks to accomplish the model learning from data , have become the promising solution for the text classification . ( cf . Fig [ reference ] ) were 10.13 and 9.37 , respectively , which is no better than SVM bow2 . Since excellent performances were reported on short sentence classification , we presume that their model is optimized for short sentences , but not for text categorization in general . CNN training is known to be expensive , compared with , e.g. , linear models – linear SVM with bow3 on IMDB only takes 9 minutes using SVMlight ( single - core ) on a high - end Intel CPU . Nevertheless , with our code on GPU , CNN training only takes minutes ( to a few hours ) on these datasets shown in Figure [ reference ] . ‘ R - CNN with AlexNet ’ ’ with the distinction made clear by the context . During R - CNN training AlexNet is fine - tuned for the ( pedestrian ) detection task , and in a second step , the softmax output is replaced by a linear SVM . Unless otherwise specified , we use the default parameters of the open source , Caffe based , R - CNN implementation . However , such retraining requires a large target domain training set to fine tune all parameters in the deep network . In this paper , we focus on updating the linear SVM only , as this classifier has a regularization structure that has been shown to perform well for unbalanced training sets with few positive examples ( e.g. from media in a template ) and many negative examples . Finally , we note that the approach of defining a similarity function for face verification using linear SVMs trained on a large negative set was originally proposed as one - shot similarity ( OSS ) . We compare with several baseline methods , including : In SVM - indep , SVM classifier is built with target - independent features , such as unigram , bigram , punctuations , emoticons , hashtags , the numbers of positive or negative words in General Inquirer sentiment lexicon . In SVM - dep , target - dependent features are also concatenated as the feature representation . Fast R - CNN uses the softmax classifier learnt during fine - tuning instead of training one - vs - rest linear SVMs post - hoc , as was done in R - CNN and SPPnet . To understand the impact of this choice , we implemented post - hoc SVM training with hard negative mining in Fast R - CNN . We use the same training algorithm and hyper - parameters as in R - CNN . For the sake of fair comparison , the conditional GAN shared the same architecture as the CLS - GAN . On the other hand , the DCGAN algorithm max - pooled the discriminator ’s convolution features from all layers to grids as the image features , and a L2 - SVM was then trained to classify images . The DCGAN was an unsupervised model which had shown competitive performance on generating photo - realistic images . ; khUðxiÞ;wni ð12Þ where m is the number of training patterns ; C is the cost parameter . The cost parameter C of the SVM formulation in Eq . ( 10 ) controls the penalty paid by the SVM for misclassifying a training point and thus , the complexity of the prediction function . One aim of this work is to move towards eliminating the human labeling component from zero - shot learning , e.g. by using alternative sources of information . On the other hand , large - margin support vector machines ( SVM ) operate with labeled training images , so a lack of labels limits their use for this task . Inspired by previous work on label embedding and structured SVMs , we propose to use a Structured Joint Embedding ( SJE ) framework ( Fig . It has obtained considerable attention due to its broad applications in natural language processing . Most existing studies set up sentiment classifiers using supervised machine learning approaches , such as support vector machine ( SVM ) , convolutional neural network ( CNN ) , long short - term memory ( LSTM ) , Tree - LSTM , and attention - based methods . Despite the remarkable progress made by the previous work , we argue that sentiment analysis still remains a challenge . These representations are provided to the fully - connected layers of the network . Then we train a binary linear SVM classifier for each category on these features . Our implementation of the SVM training follows . This paper describes SVM-based parameter setting of self-q uotient ε-filter ( SQEF ) , and its application to noise robust human detection combining SQEF , histograms of oriented gradients ( HOG ) , and support vector machine ( SVM ) . Although human detection combining HOG and S VM is a powerful approach , as it uses local intensity gradients , it is difficult to handle noise co rrupted images . We first describe and analyse an SDN-based traffic flow matching control system that applies a reinforcement learning approach based on Q-learning algorithm in order to maximize the traffic flow granularity . It also considers the forwarding performance status of the SDN switches derived from a Support Vector Machine based algorithm . Next , we outline the Q-DATA framework that incorporates the optimal traffic flow matching policy derived from the traffic flow matching control system to efficiently provide the most detailed traffic flow information that other mechanisms require . The knowledge of a protein 's structure is crucial in understanding its biological role . In this work , we present a support vector machine based method for recognising a protein 's fold from sequence information alone , where this sequence has less similarity with sequences of known structures . We have focused on improving multi-class classification , parameter tuning , descriptor design , and feature selection . The availability of screening data including those in organized databases greatly facilitates exploration of newer computational approaches . In this paper , we report the development of a prediction method based on a support machine vector algorithm . The method uses a combination of descriptors , encoding both the type of toxicophore as well as the position of the toxicophore in the drug molecule , thus considering both the pharmacophore and the three-dimensional shape information of the molecule . Signal strength measurement is part of the normal operating mode of wireless equipment , in particular Wi-Fi , so that no special-purpose hardware is required . The proposed techniques , based on the Support Vector Machine paradigm , have been implemented and compared , on the same data set , with other approaches considered in scientific literature . Tests performed in a real-world environment show that results are comparable , with the advantage of a low algorithmic complexity in the normal operating phase . The algorithm makes use of the Kalman filter and is optimal in a minimum variance framework . It extends the support vector machine paradigm to applications involving real-time and non-stationary signal processing . It also provides a computationally efficient alternative to the problem of quadratic optimisation . So when it is applied to deal with nonlinear problems , by the use of a kernel instead of the high-dimensional inner product space calculation , the nonlinear function can be changed into a high-dimensional linear problem , which could resolve the problem of dimensionality and local extrema [ 13 - 18 ] . Principle of Support Vector Machine Support vector machine is developed from the optimal classification in linearly separable cases [ 19 - 21 ] . Its essence is to find a rule to divide space point Rd into two parts . First , a couple of AdaBoost classifiers trained with Haar-like features are used to preselect possible eye locations . Then , a Support Vector Machine machine that uses Histograms of Oriented Gradients descriptors is used to obtain the best pair of eyes among all possible combinations of preselected eyes . Finally , we compare the eye detection results with three state-of-the-art works and a commercial software . According to the growing Web data mining present situation , this paper proposes a based on support vector machine and clustering Web mining new methods and based on support vector machine support vector wo n't appear in the two types of samples is outside of the division between correct theory , through the introduction of the clustering of centroid , class radius , class from the concepts , such as the center , which could well solve quickly and accurately remove the support vector problems , ensure the algorithm generalization . The experiment showed that the improved algorithm can quickly and accurately to the training sample of exclusion and has good generalization problems to solve . By bridging a gap between kernel methods and neural networks , we believe that we are opening a fruitful research direction for the future . Our network is learned without supervision since the label information is only used subsequently in a support vector machine ( SVM ) . Yet , we achieve competitive results on several datasets such as MNIST , CIFAR - 10 and STL - 10 with simple architectures , few parameters to learn , and no data augmentation . From the patches which have the most variance between two images , features are extracted . The dimensionality of the features is reduced and then given to a Support Vector Machine ( SVM ) . To evaluate the method , a 10 - fold cross - validation is applied . Target - dependent sentiment classification is typically regarded as a kind of text classification problem in literature . Majority of existing studies build sentiment classifiers with supervised machine learning approach , such as feature based Supported Vector Machine or neural network approaches . Despite the effectiveness of these approaches , we argue that target - dependent sentiment classification remains a challenge : how to effectively model the semantic relatedness of a target word with its context words in a sentence . Target - dependent sentiment classification is typically regarded as a kind of text classification problem in literature . Therefore , standard text classification approach such as feature - based Supported Vector Machine can be naturally employed to build a sentiment classifier . Despite the effectiveness of feature engineering , it is labor intensive and unable to discover the discriminative or explanatory factors of data . To obtain for our model we used Logistic Regression . The resulting plot can be seen in Figure [ reference ] , where we have also added the plot from DANN 2015arXiv150507818 G , where they used a linear Support Vector Machine for the classifier , as a reference . It can be seen that our VFAE model can factor out the information about better , since the PAD scores on our new representation are , overall , lower than the ones obtained from the DANN architecture . The main objective of this work is to develop a music emotion recognition and genre classification method by using Gaussian process and neural network . The traditional methods for the above tasks are make use of support vector machine and two dimensional valence arousal models . To assess the large sample performance , we derive the Cramer-Rao bound ( CRB ) for the blind CFO estimation problem . When deriving the CRB , the transmitted OFDM modulated signal is assumed to be a Gaussian process . Since real-valued constellations are used , the received signal is non-circular . To achieve this , the target function is modelled as a linear transformation of an underlying function . This transformation is chosen such that any prediction of the target function is guaranteed to satisfy the constraints and can be determined from known physics or , more generally , by following a constructive procedure that was previously presented for Gaussian processes . The approach is demonstrated on simulated and real-data examples . CDE is a challenging task as there is a fundamental trade-off between model complexity , representational capacity and overfitting . In this work , we propose to extend the model 's input with latent variables and use Gaussian processes ( GP ) to map this augmented input onto samples from the conditional distribution . Our Bayesian approach allows for the modeling of small datasets , but we also provide the machinery for it to be applied to big data using stochastic variational inference . These conditions include fog and night environments . For visualization under fog , we assume that fog is the unknown scattering media so that we use peplography technique which estimates the scattering media by Gaussian random process and detects ballistic photons from the scattering media by photon counting imaging . In addition , we use photon counting imaging with Bayesian estimation and adaptive statistical parameters for night vision . Finally , a database of hyperspectral human skin images was constructed to assist the development of skin detection techniques , and the challenges faced during its construction are discussed . Preliminary analysis of these data has tied specific skin regions ( e.g. nose , ears , lips and knees ) to distinct endmembers and reflectance histogram modes , and that the commonly-used Gaussian random process model does not sufficiently characterise this spatial heterogeneity of skin . While a successful method for skin detection was not established , the practical tools provided by this research make significant contributions towards realising this goal . This ranking function is more efficient and accurate than the function leaned by proposed approaches . Experimental results on document retrieval dataset show that the generalization performance of it is competitive with SVM-based ranking method and Gaussian process based method . To address this challenge , we propose the Pareto Active Learning ( PAL ) algorithm which intelligently samples the design space to predict the Pareto-optimal set . Key features of PAL include ( 1 ) modeling the objectives as samples from a Gaussian process distribution to capture structure and accomodate noisy evaluation ; ( 2 ) a method to carefully choose the next design to evaluate to maximize progress ; and ( 3 ) the ability to control prediction accuracy and sampling cost . We provide theoretical bounds on PAL 's sampling cost required to achieve a desired accuracy . In addition , the choice of the inner product makes it possible to avoid numerical integration . As an important application , we consider the case when $ f(t)=\sqrt{t}$ and $ A$ is a sparse , symmetric positive-definite matrix , which arises in sampling from a Gaussian process distribution . The covariance matrix of the distribution is defined by using a covariance function that has a compact support , at a very large number of sites that are on a regular or irregular grid . In ANOVA tests both measures show statistical difference in their values for nonseizure and seizure EEG . A comparison with a similar Autoregressive ( AR ) modelling approach shows that Gaussian Process model methods show great promise in real-time neonatal seizure detection . Simulated examples show that , compared with one-stage procedures , the GSinCE procedure provides accurate screening while reducing computational effort . In this dissertation , the sensitivity indices used as screening measures are computed in a Gaussian process model framework . This approach is known to be computationally efficient by using small numbers of expensive computer code runs for the estimation of sensitivity indices . A simple post-detection integration at the secondary user receiver using the FB output signals is employed for signal demodulation of differential phase-shift keying symbols transmitted on a set of subcarriers . A constrained optimization design for the FB prototype filter coefficients is proposed which combines time-frequency ( TF ) concentration properties for spectrum sensing with system specifications in terms of the maximum allowed bit-error rate ( BER ) for the fading channel characteristics and models the primary user signal as a Gaussian distributed process . The FB TF resolution properties as well as the BER performance are investigated for transmission with a raw bit rate of 30Mbit/s in an indoor scenario . Analysis of real imagery suggests that these expressions are limited by the use of first-order statistics to quantify the structure of both the target and the background . In most imagery for example , the background can not be adequately described by a single Gaussian-like process and thus , violate fundamental assumptions of these approaches . This paper summarizes the results of a multi-year effort that has resulted in the development of a Gray-Level Co-occurrence Matrix based Trackability Metric . It can help maximize the wireless sensor network lifetime while gurantee the quality of SHM . We model the structure as Gaussian Process distributed and nodes transmit to gateway ( GW ) . Through extensive simulations , we show that our method outperforms the benchmark method which make it suitable for real experiment setting . Therefore , this paper proposes to combine neural networks with Gaussian processes . While the neural network part works as an encoder to extract information from the input data and encodes it in a lower-dimensional space , the final Gaussian process part takes this information and treats it in a probabilistic fashion . The main advantage is that in this way not only a mean value is predicted that represents the lane change probability but a distribution of the prediction consisting of the mean and the standard deviation . The momentum of and weight decay of are fixed for all datasets as another regularizer besides dropout . All the initial pooling filters and pooling masks have values sampled from a Gaussian distribution with zero mean and standard deviation . We use these hyperparameter settings for all experiments reported in Tables [ reference ] , [ reference ] , and [ reference ] . During training , the loss of our scale - forecast network is a binary multi - class cross entropy loss : where are the ground truth label and prediction of the - th scale , respectively . Note that the ground truth label for the neighbouring scales of an occurring scale ( ) is not zero and is defined as the Gaussian sampling score : where are hyperparameters in the Gaussian distribution and denotes the neighbour set . Here we use as the neighbour size and set , to , , respectively . For binary images we use the Bernoulli distribution . For natural images we use the Gaussian distribution with mean and log variance given by splitting the vector to obtain the input cost and the total cost : where the handling of real valued - ness of the inputs ( [ reference ] , [ reference ] ) is explained below , and being the standard setting . The algorithm is schematically illustrated in the first layer of Figure [ reference ] . Improving Linear 3DMM . With PCA bases , the statistical distribution underlying 3DMM is Gaussian . Koppen et al . argue that single - mode Gaussian ca n’t represent real - world distribution . There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand . In this work , we consider this problem through the framework of Bayesian optimization , in which a learning algorithm ’s generalization performance is modeled as a sample from a Gaussian process ( GP ) . We show that certain choices for the nature of the GP , such as the type of kernel and the treatment of its hyperparameters , can play a crucial role in obtaining a good optimizer that can achieve expertlevel performance . “ ... ( Gaussian Processes ) are inadequate for doing speech and vision . I still think they 're inadequate for doing speech and vision . Furthermore the distributed representation of a neural network would allow us to better resolve the dependencies between and thus yielding a better disentangling between the independent factors and . By choosing a Gaussian posterior and standard isotropic Gaussian prior we can obtain the following lower bound : with and with being an appropriate probability distribution for the data we are modelling . subsection : Semi - Supervised model We produced an empirical win - probability matrix by summing wins across all games and used this matrix to evaluate Elo scores . A ranking difference of 400 corresponds to the odds of winning of 10:1 under the Gaussian assumption . subsection : Contextual priority tree The kernel size is fixed to 15 15 . To further expand the degradation space , we also consider a more general kernel assumption , , anisotropic Gaussian , which is characterized by a Gaussian probability density function with zero mean and varying covariance matrix . The space of such Gaussian kernel is determined by rotation angle of the eigenvectors of and scaling of corresponding eigenvalues . By contrast , bootstrapped neural networks may include different subsets of this noisy data and so may produce a more intuitive uncertainty estimates for our settings . Note this is n’t necessarily a failure of dropout to approximate a Gaussian process posterior , but this artefact could be shared by any homoskedastic posterior . The authors of propose a heteroskedastic variant which can help , but does not address the fundamental issue that for large networks trained to convergence all dropout samples may converge to every single datapoint … even the outliers . We train the full model end - to - end in a single step of optimization . We train the convolutional and recurrent network from scratch with all weights initialized from a Gaussian with . Gradients are computed using and flow through the recurrent and convolutional networks . PCA groups textures according to a natural similarity . Classification of the recordings using k nearest neighbors shows a high accuracy for PCA data . Clustering of the PCA data shows that similar discs are grouped together with few classification errors . We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital Malaysia , and measuring the accuracy of test results . The best achieved classification rates are 97 % , 98 % , and 79 % for Support vector machines , Radial Basis Function neural network , and K-Nearest Neighbors algorithm respectively . Keywords — K-Nearest Neighbors , Neural Network , Radial Basis Function , Red blood cells , Support vector machine . Given a target image , we directly take the LR ’s output as the predicted label distribution . We also test a nonparametric method by simply retrieving the nearest neighbors ( NNs ) for a target image and then transferring the mean of the NNs ’ label distributions to the target image . We use the distance for the NN retrieval . bibliography : References section : Additional Samples , Interpolations , and Nearest Neighbors from ImageNet models section : Architectural details We observe a Consistency score of which is on par with the models in . We quantify that our models are more effective than exemplar based Super Resolution techniques like Nearest Neighbors , which perform a naive look - up of the training data to find the high resolution output . We take a bicubic down - sampled version of our high resolution sample , find the nearest low resolution input image in the training data for that sample , and calculate the MS - SSIM score between the high resolution sample and the corresponding high resolution image in the training data . Baseline Methods We compare our method with five methods for unsupervised domain adaptation including state - of - the art methods in visual domain adaptation ; Maximum Mean Discrepancy ( MMD ) [ reference ] , Domain Adversarial Neural Network ( DANN ) [ reference ] , Deep Reconstruction Classification Network ( DRCN ) [ reference ] , Domain Separation Networks ( DSN ) [ reference ] , and k - Nearest Neighbor based adaptation ( kNN - Ad ) In most of our experiments , input shapes are represented as point cloud with around points . Given an input shape point cloud , we build a k - nearest neighbor graph first . We use in all our experiments . Afterward , some statistical features were extracted . Next , k-nearest neighbor ( k-NN ) and decision trees classifiers were used to classify the normal and abnormal lesions . Moreover , multiple classifier systems ( MCS ) was constructed as it usually improves the classification results . The main objective of this study was to evaluate the effectiveness of adding feature variables , such as forest type information and topographic- and climatic-environmental factors to satellite image data , on the accuracy of stand volume estimates made with the k-nearest neighbor ( k-NN ) technique in southwestern Japan . Data from the Forest Resources Monitoring Survey — a national plot sampling survey in Japan — was used as in situ data in this study . This paper presents effective state-of-the-art techniques and compares them to two novel , salient modifications . These are a normalized compactness measure , which has salient properties as it is non-parametric , easy-to-use , fine-grained , competitive performing , and the sum-volumetric k-NN classifier . The aims of this paper are to compare the computation complexities , discriminant properties or capabilities , and sensitivity to control parameter . These extracted features are ranked using p-value of the t-test and fed to various supervised classifiers viz . Decision Tree ( DT ) , Nearest Neighbour ( k-NN ) , Naive Bayes ( NB ) , Probabilistic Neural Network ( PNN ) and Support Vector Machine ( SVM ) to classify normal and AMD classes . The performance of the system is evaluated using both private ( Kasturba Medical Hospital , Manipal , India ) and public domain datasets viz . Experiment results reveal that the proposed one-class classification method works well even when the number of training samples is small ( & le;10 ) . Moreover , we noted that the proposed one-class classification method is suitable for document classification and provides better result compared to one-class k-nearest neighbor ( k-NN ) classifier . Nonparametric classification procedures derived from the multivariate kernel density estimate are examined . Conditions for weak and strong Bayes risk consistencies are given . Good classification accuracy on crop species identification of some 40 000 samples in a five square mile area was achieved in one example . Other examples show results in feature selection and nonparametric classifier algorithms . This survey paper demonstrates study of parametric and non-parametric classification techniques . Study isolates parametric and nonparametric classification techniques which are employed in classification phase and offers tree depictions of such methods . For comparative study 9 classifiers are utilized among which 6 belongs to parametric category and remaining 3 belongs to non-parametric category.9 classifiers considered here are super simple , their interpretation is nice probabilistic , used mostly and well known . Kohonen ’s Learning Vector Quantization is a nonparametric classification scheme which classifies observations by comparing them to k templates called Voronoi vectors . The locations of these vectors are determined from past labeled data through a learning algorithm . In the field of statistical discrimination nearest neighbor methods are a well known , quite simple but successful nonparametric classification tool . If the number of predictors increases , however , predictive power normally deteriorates . The proposed method combines the support vector data description-based test statistic with an adaptive step-down procedure to identify the faults . By using a nonparametric one-class classification method , the proposed approach does not rely on any distribution assumption . Compared with the existing distribution free methods , the proposed method has much more stable performance when the number of faults is more than one . Apart from parametric methods for the evaluation of prognostic factors ( e.g. discriminant analysis , logistic regression or Cox-regression ) there are ( nonparametric ) statistical classification algorithms that work without specific requirements to the distribution of the underlying data . Such a method is that of “ Classification and Regression Trees ” ( CART ) . This paper presents a method for incorporating prior probabilities in remote-sensing-based land cover classification using a supervised decision-tree classification algorithm . The method allows robust probabilities of class membership to be estimated from nonparametric supervised classification algorithms using a technique known as boosting . By using this approach in association with Bayes ' rule , poorly separable classes can be distinguished based on ancillary information . This represents an ambitious goal to generate samples from any distributions . However , it in turn requires a non - parametric discriminator to prove the distributional consistency between generated and real samples by assuming the model has infinite capacity ( see Section 4 of ) . This is a too strong assumption to establish the theoretical basis for the GAN .
The algorithm converges to a stably local optimal solution under comparatively lenient conditions . In addition , the optimization problem to be solved includes the original SVM as a special case . However , since the amount of computation increases as the dimensions of the input space increase in the proposed algorithm , this paper proposes a simplified algorithm obtained by combining the original SVM and the abridged proposed algorithm .
Finally , the best-proposed models were examined using a few examples of real lost circulation cases from the field . The results of the analysis have revealed that both ANNs and SVM approaches can be of great use , with the SVM results being more promising . The application of the machine learning methods could assist drilling engineers in modifying drilling parameters to minimize the likelihood of lost circulation .
There are two rows and four columns of sub - figures . The top row shows the results of KNN , and the bottom one shows results of SVM . Each column gives the result on one dataset .
Bag - of - words is a very efficient way to conduct the feature engineering . SVM and Naive Bayes are constantly the classifier . The latter , deep neural models , taking advantage of neural networks to accomplish the model learning from data , have become the promising solution for the text classification .
( cf . Fig [ reference ] ) were 10.13 and 9.37 , respectively , which is no better than SVM bow2 . Since excellent performances were reported on short sentence classification , we presume that their model is optimized for short sentences , but not for text categorization in general .
CNN training is known to be expensive , compared with , e.g. , linear models – linear SVM with bow3 on IMDB only takes 9 minutes using SVMlight ( single - core ) on a high - end Intel CPU . Nevertheless , with our code on GPU , CNN training only takes minutes ( to a few hours ) on these datasets shown in Figure [ reference ] .
‘ R - CNN with AlexNet ’ ’ with the distinction made clear by the context . During R - CNN training AlexNet is fine - tuned for the ( pedestrian ) detection task , and in a second step , the softmax output is replaced by a linear SVM . Unless otherwise specified , we use the default parameters of the open source , Caffe based , R - CNN implementation .
However , such retraining requires a large target domain training set to fine tune all parameters in the deep network . In this paper , we focus on updating the linear SVM only , as this classifier has a regularization structure that has been shown to perform well for unbalanced training sets with few positive examples ( e.g. from media in a template ) and many negative examples . Finally , we note that the approach of defining a similarity function for face verification using linear SVMs trained on a large negative set was originally proposed as one - shot similarity ( OSS ) .
We compare with several baseline methods , including : In SVM - indep , SVM classifier is built with target - independent features , such as unigram , bigram , punctuations , emoticons , hashtags , the numbers of positive or negative words in General Inquirer sentiment lexicon . In SVM - dep , target - dependent features are also concatenated as the feature representation .
Fast R - CNN uses the softmax classifier learnt during fine - tuning instead of training one - vs - rest linear SVMs post - hoc , as was done in R - CNN and SPPnet . To understand the impact of this choice , we implemented post - hoc SVM training with hard negative mining in Fast R - CNN . We use the same training algorithm and hyper - parameters as in R - CNN .
For the sake of fair comparison , the conditional GAN shared the same architecture as the CLS - GAN . On the other hand , the DCGAN algorithm max - pooled the discriminator ’s convolution features from all layers to grids as the image features , and a L2 - SVM was then trained to classify images . The DCGAN was an unsupervised model which had shown competitive performance on generating photo - realistic images .
; khUðxiÞ;wni ð12Þ where m is the number of training patterns ; C is the cost parameter . The cost parameter C of the SVM formulation in Eq . ( 10 ) controls the penalty paid by the SVM for misclassifying a training point and thus , the complexity of the prediction function .
One aim of this work is to move towards eliminating the human labeling component from zero - shot learning , e.g. by using alternative sources of information . On the other hand , large - margin support vector machines ( SVM ) operate with labeled training images , so a lack of labels limits their use for this task . Inspired by previous work on label embedding and structured SVMs , we propose to use a Structured Joint Embedding ( SJE ) framework ( Fig .
It has obtained considerable attention due to its broad applications in natural language processing . Most existing studies set up sentiment classifiers using supervised machine learning approaches , such as support vector machine ( SVM ) , convolutional neural network ( CNN ) , long short - term memory ( LSTM ) , Tree - LSTM , and attention - based methods . Despite the remarkable progress made by the previous work , we argue that sentiment analysis still remains a challenge .
These representations are provided to the fully - connected layers of the network . Then we train a binary linear SVM classifier for each category on these features . Our implementation of the SVM training follows .
This paper describes SVM-based parameter setting of self-q uotient ε-filter ( SQEF ) , and its application to noise robust human detection combining SQEF , histograms of oriented gradients ( HOG ) , and support vector machine ( SVM ) . Although human detection combining HOG and S VM is a powerful approach , as it uses local intensity gradients , it is difficult to handle noise co rrupted images .
We first describe and analyse an SDN-based traffic flow matching control system that applies a reinforcement learning approach based on Q-learning algorithm in order to maximize the traffic flow granularity . It also considers the forwarding performance status of the SDN switches derived from a Support Vector Machine based algorithm . Next , we outline the Q-DATA framework that incorporates the optimal traffic flow matching policy derived from the traffic flow matching control system to efficiently provide the most detailed traffic flow information that other mechanisms require .
The knowledge of a protein 's structure is crucial in understanding its biological role . In this work , we present a support vector machine based method for recognising a protein 's fold from sequence information alone , where this sequence has less similarity with sequences of known structures . We have focused on improving multi-class classification , parameter tuning , descriptor design , and feature selection .
The availability of screening data including those in organized databases greatly facilitates exploration of newer computational approaches . In this paper , we report the development of a prediction method based on a support machine vector algorithm . The method uses a combination of descriptors , encoding both the type of toxicophore as well as the position of the toxicophore in the drug molecule , thus considering both the pharmacophore and the three-dimensional shape information of the molecule .
Signal strength measurement is part of the normal operating mode of wireless equipment , in particular Wi-Fi , so that no special-purpose hardware is required . The proposed techniques , based on the Support Vector Machine paradigm , have been implemented and compared , on the same data set , with other approaches considered in scientific literature . Tests performed in a real-world environment show that results are comparable , with the advantage of a low algorithmic complexity in the normal operating phase .
The algorithm makes use of the Kalman filter and is optimal in a minimum variance framework . It extends the support vector machine paradigm to applications involving real-time and non-stationary signal processing . It also provides a computationally efficient alternative to the problem of quadratic optimisation .
So when it is applied to deal with nonlinear problems , by the use of a kernel instead of the high-dimensional inner product space calculation , the nonlinear function can be changed into a high-dimensional linear problem , which could resolve the problem of dimensionality and local extrema [ 13 - 18 ] . Principle of Support Vector Machine Support vector machine is developed from the optimal classification in linearly separable cases [ 19 - 21 ] . Its essence is to find a rule to divide space point Rd into two parts .
First , a couple of AdaBoost classifiers trained with Haar-like features are used to preselect possible eye locations . Then , a Support Vector Machine machine that uses Histograms of Oriented Gradients descriptors is used to obtain the best pair of eyes among all possible combinations of preselected eyes . Finally , we compare the eye detection results with three state-of-the-art works and a commercial software .
According to the growing Web data mining present situation , this paper proposes a based on support vector machine and clustering Web mining new methods and based on support vector machine support vector wo n't appear in the two types of samples is outside of the division between correct theory , through the introduction of the clustering of centroid , class radius , class from the concepts , such as the center , which could well solve quickly and accurately remove the support vector problems , ensure the algorithm generalization . The experiment showed that the improved algorithm can quickly and accurately to the training sample of exclusion and has good generalization problems to solve .
By bridging a gap between kernel methods and neural networks , we believe that we are opening a fruitful research direction for the future . Our network is learned without supervision since the label information is only used subsequently in a support vector machine ( SVM ) . Yet , we achieve competitive results on several datasets such as MNIST , CIFAR - 10 and STL - 10 with simple architectures , few parameters to learn , and no data augmentation .
From the patches which have the most variance between two images , features are extracted . The dimensionality of the features is reduced and then given to a Support Vector Machine ( SVM ) . To evaluate the method , a 10 - fold cross - validation is applied .
Target - dependent sentiment classification is typically regarded as a kind of text classification problem in literature . Majority of existing studies build sentiment classifiers with supervised machine learning approach , such as feature based Supported Vector Machine or neural network approaches . Despite the effectiveness of these approaches , we argue that target - dependent sentiment classification remains a challenge : how to effectively model the semantic relatedness of a target word with its context words in a sentence .
Target - dependent sentiment classification is typically regarded as a kind of text classification problem in literature . Therefore , standard text classification approach such as feature - based Supported Vector Machine can be naturally employed to build a sentiment classifier . Despite the effectiveness of feature engineering , it is labor intensive and unable to discover the discriminative or explanatory factors of data .
To obtain for our model we used Logistic Regression . The resulting plot can be seen in Figure [ reference ] , where we have also added the plot from DANN 2015arXiv150507818 G , where they used a linear Support Vector Machine for the classifier , as a reference . It can be seen that our VFAE model can factor out the information about better , since the PAD scores on our new representation are , overall , lower than the ones obtained from the DANN architecture .
The main objective of this work is to develop a music emotion recognition and genre classification method by using Gaussian process and neural network . The traditional methods for the above tasks are make use of support vector machine and two dimensional valence arousal models .
To assess the large sample performance , we derive the Cramer-Rao bound ( CRB ) for the blind CFO estimation problem . When deriving the CRB , the transmitted OFDM modulated signal is assumed to be a Gaussian process . Since real-valued constellations are used , the received signal is non-circular .
To achieve this , the target function is modelled as a linear transformation of an underlying function . This transformation is chosen such that any prediction of the target function is guaranteed to satisfy the constraints and can be determined from known physics or , more generally , by following a constructive procedure that was previously presented for Gaussian processes . The approach is demonstrated on simulated and real-data examples .
CDE is a challenging task as there is a fundamental trade-off between model complexity , representational capacity and overfitting . In this work , we propose to extend the model 's input with latent variables and use Gaussian processes ( GP ) to map this augmented input onto samples from the conditional distribution . Our Bayesian approach allows for the modeling of small datasets , but we also provide the machinery for it to be applied to big data using stochastic variational inference .
These conditions include fog and night environments . For visualization under fog , we assume that fog is the unknown scattering media so that we use peplography technique which estimates the scattering media by Gaussian random process and detects ballistic photons from the scattering media by photon counting imaging . In addition , we use photon counting imaging with Bayesian estimation and adaptive statistical parameters for night vision .
Finally , a database of hyperspectral human skin images was constructed to assist the development of skin detection techniques , and the challenges faced during its construction are discussed . Preliminary analysis of these data has tied specific skin regions ( e.g. nose , ears , lips and knees ) to distinct endmembers and reflectance histogram modes , and that the commonly-used Gaussian random process model does not sufficiently characterise this spatial heterogeneity of skin . While a successful method for skin detection was not established , the practical tools provided by this research make significant contributions towards realising this goal .
This ranking function is more efficient and accurate than the function leaned by proposed approaches . Experimental results on document retrieval dataset show that the generalization performance of it is competitive with SVM-based ranking method and Gaussian process based method .
To address this challenge , we propose the Pareto Active Learning ( PAL ) algorithm which intelligently samples the design space to predict the Pareto-optimal set . Key features of PAL include ( 1 ) modeling the objectives as samples from a Gaussian process distribution to capture structure and accomodate noisy evaluation ; ( 2 ) a method to carefully choose the next design to evaluate to maximize progress ; and ( 3 ) the ability to control prediction accuracy and sampling cost . We provide theoretical bounds on PAL 's sampling cost required to achieve a desired accuracy .
In addition , the choice of the inner product makes it possible to avoid numerical integration . As an important application , we consider the case when $ f(t)=\sqrt{t}$ and $ A$ is a sparse , symmetric positive-definite matrix , which arises in sampling from a Gaussian process distribution . The covariance matrix of the distribution is defined by using a covariance function that has a compact support , at a very large number of sites that are on a regular or irregular grid .
In ANOVA tests both measures show statistical difference in their values for nonseizure and seizure EEG . A comparison with a similar Autoregressive ( AR ) modelling approach shows that Gaussian Process model methods show great promise in real-time neonatal seizure detection .
Simulated examples show that , compared with one-stage procedures , the GSinCE procedure provides accurate screening while reducing computational effort . In this dissertation , the sensitivity indices used as screening measures are computed in a Gaussian process model framework . This approach is known to be computationally efficient by using small numbers of expensive computer code runs for the estimation of sensitivity indices .
A simple post-detection integration at the secondary user receiver using the FB output signals is employed for signal demodulation of differential phase-shift keying symbols transmitted on a set of subcarriers . A constrained optimization design for the FB prototype filter coefficients is proposed which combines time-frequency ( TF ) concentration properties for spectrum sensing with system specifications in terms of the maximum allowed bit-error rate ( BER ) for the fading channel characteristics and models the primary user signal as a Gaussian distributed process . The FB TF resolution properties as well as the BER performance are investigated for transmission with a raw bit rate of 30Mbit/s in an indoor scenario .
Analysis of real imagery suggests that these expressions are limited by the use of first-order statistics to quantify the structure of both the target and the background . In most imagery for example , the background can not be adequately described by a single Gaussian-like process and thus , violate fundamental assumptions of these approaches . This paper summarizes the results of a multi-year effort that has resulted in the development of a Gray-Level Co-occurrence Matrix based Trackability Metric .
It can help maximize the wireless sensor network lifetime while gurantee the quality of SHM . We model the structure as Gaussian Process distributed and nodes transmit to gateway ( GW ) . Through extensive simulations , we show that our method outperforms the benchmark method which make it suitable for real experiment setting .
Therefore , this paper proposes to combine neural networks with Gaussian processes . While the neural network part works as an encoder to extract information from the input data and encodes it in a lower-dimensional space , the final Gaussian process part takes this information and treats it in a probabilistic fashion . The main advantage is that in this way not only a mean value is predicted that represents the lane change probability but a distribution of the prediction consisting of the mean and the standard deviation .
The momentum of and weight decay of are fixed for all datasets as another regularizer besides dropout . All the initial pooling filters and pooling masks have values sampled from a Gaussian distribution with zero mean and standard deviation . We use these hyperparameter settings for all experiments reported in Tables [ reference ] , [ reference ] , and [ reference ] .
During training , the loss of our scale - forecast network is a binary multi - class cross entropy loss : where are the ground truth label and prediction of the - th scale , respectively . Note that the ground truth label for the neighbouring scales of an occurring scale ( ) is not zero and is defined as the Gaussian sampling score : where are hyperparameters in the Gaussian distribution and denotes the neighbour set . Here we use as the neighbour size and set , to , , respectively .
For binary images we use the Bernoulli distribution . For natural images we use the Gaussian distribution with mean and log variance given by splitting the vector to obtain the input cost and the total cost : where the handling of real valued - ness of the inputs ( [ reference ] , [ reference ] ) is explained below , and being the standard setting . The algorithm is schematically illustrated in the first layer of Figure [ reference ] .
Improving Linear 3DMM . With PCA bases , the statistical distribution underlying 3DMM is Gaussian . Koppen et al . argue that single - mode Gaussian ca n’t represent real - world distribution .
There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand . In this work , we consider this problem through the framework of Bayesian optimization , in which a learning algorithm ’s generalization performance is modeled as a sample from a Gaussian process ( GP ) . We show that certain choices for the nature of the GP , such as the type of kernel and the treatment of its hyperparameters , can play a crucial role in obtaining a good optimizer that can achieve expertlevel performance .
“ ... ( Gaussian Processes ) are inadequate for doing speech and vision . I still think they 're inadequate for doing speech and vision .
Furthermore the distributed representation of a neural network would allow us to better resolve the dependencies between and thus yielding a better disentangling between the independent factors and . By choosing a Gaussian posterior and standard isotropic Gaussian prior we can obtain the following lower bound : with and with being an appropriate probability distribution for the data we are modelling . subsection : Semi - Supervised model
We produced an empirical win - probability matrix by summing wins across all games and used this matrix to evaluate Elo scores . A ranking difference of 400 corresponds to the odds of winning of 10:1 under the Gaussian assumption . subsection : Contextual priority tree
The kernel size is fixed to 15 15 . To further expand the degradation space , we also consider a more general kernel assumption , , anisotropic Gaussian , which is characterized by a Gaussian probability density function with zero mean and varying covariance matrix . The space of such Gaussian kernel is determined by rotation angle of the eigenvectors of and scaling of corresponding eigenvalues .
By contrast , bootstrapped neural networks may include different subsets of this noisy data and so may produce a more intuitive uncertainty estimates for our settings . Note this is n’t necessarily a failure of dropout to approximate a Gaussian process posterior , but this artefact could be shared by any homoskedastic posterior . The authors of propose a heteroskedastic variant which can help , but does not address the fundamental issue that for large networks trained to convergence all dropout samples may converge to every single datapoint … even the outliers .
We train the full model end - to - end in a single step of optimization . We train the convolutional and recurrent network from scratch with all weights initialized from a Gaussian with . Gradients are computed using and flow through the recurrent and convolutional networks .
PCA groups textures according to a natural similarity . Classification of the recordings using k nearest neighbors shows a high accuracy for PCA data . Clustering of the PCA data shows that similar discs are grouped together with few classification errors .
We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital Malaysia , and measuring the accuracy of test results . The best achieved classification rates are 97 % , 98 % , and 79 % for Support vector machines , Radial Basis Function neural network , and K-Nearest Neighbors algorithm respectively . Keywords — K-Nearest Neighbors , Neural Network , Radial Basis Function , Red blood cells , Support vector machine .
Given a target image , we directly take the LR ’s output as the predicted label distribution . We also test a nonparametric method by simply retrieving the nearest neighbors ( NNs ) for a target image and then transferring the mean of the NNs ’ label distributions to the target image . We use the distance for the NN retrieval .
bibliography : References section : Additional Samples , Interpolations , and Nearest Neighbors from ImageNet models section : Architectural details
We observe a Consistency score of which is on par with the models in . We quantify that our models are more effective than exemplar based Super Resolution techniques like Nearest Neighbors , which perform a naive look - up of the training data to find the high resolution output . We take a bicubic down - sampled version of our high resolution sample , find the nearest low resolution input image in the training data for that sample , and calculate the MS - SSIM score between the high resolution sample and the corresponding high resolution image in the training data .
Baseline Methods We compare our method with five methods for unsupervised domain adaptation including state - of - the art methods in visual domain adaptation ; Maximum Mean Discrepancy ( MMD ) [ reference ] , Domain Adversarial Neural Network ( DANN ) [ reference ] , Deep Reconstruction Classification Network ( DRCN ) [ reference ] , Domain Separation Networks ( DSN ) [ reference ] , and k - Nearest Neighbor based adaptation ( kNN - Ad )
In most of our experiments , input shapes are represented as point cloud with around points . Given an input shape point cloud , we build a k - nearest neighbor graph first . We use in all our experiments .
Afterward , some statistical features were extracted . Next , k-nearest neighbor ( k-NN ) and decision trees classifiers were used to classify the normal and abnormal lesions . Moreover , multiple classifier systems ( MCS ) was constructed as it usually improves the classification results .
The main objective of this study was to evaluate the effectiveness of adding feature variables , such as forest type information and topographic- and climatic-environmental factors to satellite image data , on the accuracy of stand volume estimates made with the k-nearest neighbor ( k-NN ) technique in southwestern Japan . Data from the Forest Resources Monitoring Survey — a national plot sampling survey in Japan — was used as in situ data in this study .
This paper presents effective state-of-the-art techniques and compares them to two novel , salient modifications . These are a normalized compactness measure , which has salient properties as it is non-parametric , easy-to-use , fine-grained , competitive performing , and the sum-volumetric k-NN classifier . The aims of this paper are to compare the computation complexities , discriminant properties or capabilities , and sensitivity to control parameter .
These extracted features are ranked using p-value of the t-test and fed to various supervised classifiers viz . Decision Tree ( DT ) , Nearest Neighbour ( k-NN ) , Naive Bayes ( NB ) , Probabilistic Neural Network ( PNN ) and Support Vector Machine ( SVM ) to classify normal and AMD classes . The performance of the system is evaluated using both private ( Kasturba Medical Hospital , Manipal , India ) and public domain datasets viz .
Experiment results reveal that the proposed one-class classification method works well even when the number of training samples is small ( & le;10 ) . Moreover , we noted that the proposed one-class classification method is suitable for document classification and provides better result compared to one-class k-nearest neighbor ( k-NN ) classifier .
Nonparametric classification procedures derived from the multivariate kernel density estimate are examined . Conditions for weak and strong Bayes risk consistencies are given .
Good classification accuracy on crop species identification of some 40 000 samples in a five square mile area was achieved in one example . Other examples show results in feature selection and nonparametric classifier algorithms .
This survey paper demonstrates study of parametric and non-parametric classification techniques . Study isolates parametric and nonparametric classification techniques which are employed in classification phase and offers tree depictions of such methods . For comparative study 9 classifiers are utilized among which 6 belongs to parametric category and remaining 3 belongs to non-parametric category.9 classifiers considered here are super simple , their interpretation is nice probabilistic , used mostly and well known .
Kohonen ’s Learning Vector Quantization is a nonparametric classification scheme which classifies observations by comparing them to k templates called Voronoi vectors . The locations of these vectors are determined from past labeled data through a learning algorithm .
In the field of statistical discrimination nearest neighbor methods are a well known , quite simple but successful nonparametric classification tool . If the number of predictors increases , however , predictive power normally deteriorates .
The proposed method combines the support vector data description-based test statistic with an adaptive step-down procedure to identify the faults . By using a nonparametric one-class classification method , the proposed approach does not rely on any distribution assumption . Compared with the existing distribution free methods , the proposed method has much more stable performance when the number of faults is more than one .
Apart from parametric methods for the evaluation of prognostic factors ( e.g. discriminant analysis , logistic regression or Cox-regression ) there are ( nonparametric ) statistical classification algorithms that work without specific requirements to the distribution of the underlying data . Such a method is that of “ Classification and Regression Trees ” ( CART ) .
This paper presents a method for incorporating prior probabilities in remote-sensing-based land cover classification using a supervised decision-tree classification algorithm . The method allows robust probabilities of class membership to be estimated from nonparametric supervised classification algorithms using a technique known as boosting . By using this approach in association with Bayes ' rule , poorly separable classes can be distinguished based on ancillary information .
This represents an ambitious goal to generate samples from any distributions . However , it in turn requires a non - parametric discriminator to prove the distributional consistency between generated and real samples by assuming the model has infinite capacity ( see Section 4 of ) . This is a too strong assumption to establish the theoretical basis for the GAN .
SVM that maximizes the margin in the input space Implementing artificial neural networks and support vector machines to predict lost circulation 10, 000+ Times Accelerated Robust Subset Selection (ARSS) Explicit Interaction Model towards Text Classification Effective Use of Word Order for Text Categorization with Convolutional Neural Networks Taking a deeper look at pedestrians Template adaptation for face verification and identification Effective LSTMs for Target-Dependent Sentiment Classification Fast R-CNN Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification Evaluation of output embeddings for fine-grained image classification A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition SVM-based Parameter Setting of Self-quotient ɛ-Filter and Its Application to Noise Robust Human Detection Q-DATA: Enhanced Traffic Flow Monitoring in Software-Defined Networks applying Q-learning Improved protein fold assignment using support vector machines Substructure-Based Support Vector Machine Classifiers for Prediction of Adverse Effects in Diverse Classes of Drugs Statistical learning theory for location fingerprinting in wireless LANs Sequential support vector machines Prediction of Data Classification Based on Support Vector Machine Precise eye localization using HOG descriptors Based on support vector machines and new methods of clustering for web mining Convolutional Kernel Networks DeXpression: Deep Convolutional Neural Network for Expression Recognition Effective LSTMs for Target-Dependent Sentiment Classification The Variational Fair Autoencoder Music Genre and Emotion Recognition Using Gaussian Processes and Neural Network Performance bound for blind CFO estimation in OFDM with real-valued constellations Linearly Constrained Neural Networks Gaussian Process Conditional Density Estimation Optical 3D visualization under inclement weather conditions Hyperspectral Image Acquisition and Calibration with Application to Skin Detection Systems Preference Learning to Rank with Sparse Bayesian Active Learning for Multi-Objective Optimization Computing f(A)b via Least Squares Polynomial Approximations Gaussian Process Modelling as an Indicator of Neonatal Seizure Design and analysis of computer experiments for screening input variables Post detection integration in DFT modulated filter bank transceivers for cognitive radio Development of the gray-level co-occurrence matrix target trackability metric for imaging infrared missile systems Sensor Selection in Wireless Sensor Networks for Structural Health Monitoring Probabilistic Lane Change Prediction using Gaussian Process Neural Networks Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree Recurrent Scale Approximation for Object Detection in CNN Towards Conceptual Compression Nonlinear 3D Face Morphable Model Practical Bayesian Optimization of Machine Learning Algorithms The Variational Fair Autoencoder The Reactor: A Sample-Efficient Actor-Critic Architecture Learning a Single Convolutional Super-Resolution Network for Multiple Degradations Deep Exploration via Bootstrapped DQN Towards Viewpoint Invariant 3D Human Pose Estimation Extracting textural features from tactile sensors. Comparative Study Using WEKA for Red Blood Cells Classification Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes Large Scale GAN Training for High Fidelity Natural Image Synthesis Image Transformer Asymmetric Tri-training for Unsupervised Domain Adaptation SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation Breast Cancer Diagnosis Using an Efficient CAD System Based on Multiple Classifiers Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables Comparison of Effective Assessment Functions for Optimized Sensor System Design Local configuration pattern features for age-related macular degeneration characterization and classification A New One-Class Classification Method Based on Symbolic Representation: Application to Document Classification Classification procedures using multivariate variable kernel density estimate Information processing of remotely sensed agricultural data Evaluation and Analysis of Few Parametric and Nonparametric Classification Methods Convergence of the Vectors in Kohonen’s Learning Vector Quantization Feature Selection and Weighting by Nearest Neighbor Ensembles Special Issue on INFORMS 2015 Annual Meeting On the Validation of Classification Trees Using prior probabilities in decision-tree classification of remotely sensed data Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
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AAAI 2015 AAAI HLT-NAACL 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Image Vis. Comput. COLING 2015 IEEE International Conference on Computer Vision (ICCV) International Journal of Computer Vision Comput. Environ. Urban Syst. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ACL IEEE Transactions on Pattern Analysis and Machine Intelligence ICAART (1) ICDIP NIPS ArXiv COLING ICLR ICML (1) SPPRA BlackSeaCom ITSC AISTATS 2017 IEEE International Conference on Computer Vision (ICCV) NIPS 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition NIPS ICLR ArXiv 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition NIPS ECCV 2017 IEEE International Conference on Computer Vision (ICCV) ICLR ICML ICML 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Document Analysis Systems International Journal of Computer Vision
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