Wavelet time – frequency analysis has been widely used for machinery fault diagnosis . Mechanical vibration signals can be converted to time – frequency images using wavelet transform , so machinery fault diagnosis can be transformed to the problem of image classification . Label consistent K-SVD algorithm has been proven to be effective in image classification , which incorporates a label consistent term namely discriminative sparse code error into the objective function . In this study , we revisit their hypothesis , considering units with selectivity for lower-level features , and argue that selective units are not always harmful to the network performance . Specifically , by using DNNs trained for image classification , we analyzed the orientation selectivity of individual units . Orientation selectivity is a low-level selectivity widely studied in visual neuroscience , in which , when images of bars with several orientations are presented to the eye , many neurons in the visual cortex respond selectively to a specific orientation . Instances are labeled as positive if the word is relevant for describing the visual content of the image , and negative otherwise . This novel approach of the image classification problem poses an alternative scenario where any text-image pair can be represented in such space , so any word could be considered for describing an image . The proposed methods are diverse and competitive , showing considerable improvements over the proposed baselines . In this paper , we present a new approach that uses deep transfer learning techniques to recognize gestures based on the channel state information ( CSI ) extracted from WiFi signals . Firstly , the CSI streams of gestures are captured and the gesture segments are extracted based on the CSI amplitude changes , and then the WiFi-based gesture recognition problem is innovatively converted to an image classification problem by expressing CSI streams as an image matrix . After that , two deep transfer learning methods are applied to recognize gestures using high-level features extracted by deep convolutional neural network ( CNN ) and fine-tuned CNN models . We propose a deep learning framework for image set classification with application to face recognition . An Adaptive Deep Network Template ( ADNT ) is defined whose parameters are initialized by performing unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines ( GRBMs ) . The Riemannian kernel is shown to satisfy Mercer ’s theorem , so the CSPD matrix is symmetric and positive definite , and also lies on a Riemannian manifold . Test on three benchmark datasets shows that CSPD is both lower-dimensional and more discriminative data descriptor than standard SPD for the task of image set classification . Image set classification finds its applications in a number of real-life scenarios such as classification from surveillance videos , multi-view camera networks and personal albums . Compared with single image based classification , it offers more promises and has therefore attracted significant research attention in recent years . Unlike many existing methods which assume images of a set to lie on a certain geometric surface , this paper introduces a deep learning framework which makes no such prior assumptions and can automatically discover the underlying geometric structure . - An application to image data classification from Cherenkov telescope experiment . Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers . A new classifier for image data classification named as linear twin bounded support tensor machine ( linear TBSTM ) is proposed by adding regularization terms in objective functions , which results in the realization of structural risk minimization avoids of the singularity of matrices . We know that up to now nonlinear classifiers based on STM for image data classification are not seen more . In order to remedy this limitation , a new matrix kernel function is introduced and based on which the nonlinear version of TBSTM is studied with a detailed theoretical derivation , and then a nonlinear classifier called as nonlinear TBSTM is suggested . Third , perceiving information from different scales is essential for understanding parts as well as objects for tasks such as fine - grained classification and semantic segmentation . Thus , it is of critical importance to design good features for multi - scale stimuli for visual cognition tasks , including image classification , object detection , attention prediction , target tracking , action recognition , semantic segmentation , salient object detection . Unsurprisingly , multi - scale features have been widely used in both conventional feature design and deep learning . It also augments the sampling locations in the convolution with offsets and learns the offsets via back - propagation end - to - end . It is shown effective on image classification tasks . Two crucial differences from deformable convolution make this work less general and adaptive . The proposed LDMRBP operator is built using new neighborhood topology and new pattern encoding scheme . The adopted face recognition system consists of three stages : ( 1 ) face detection and alignment to normalize the input images to a common form if needed ; ( 2 ) feature extraction using the proposed descriptor in order to calculate the histogram , which represents the feature vector and ( 3 ) face recognition through a supervised image classification task using the simple K-Nearest Neighbors classifier . Simulated experiments on ORL , YALE and FERET under different illumination or facial expression conditions indicate that the proposed method outperforms other texture descriptors and other existing works of the literature . It first implements a uniform target detector ( UTD ) to find a set of interfering signatures , I , then another uniform I-annihilated detector ( UIAD ) to find desired target signatures . The signatures in I are combined with the UIAD found desired signatures to form a target signatures matrix M which can be used for supervised image classification . In this case , OSP can be used for this purpose . In particular , we demonstrate that embedding space projections of the decoder states of RNN Seq2Seq model trained on sequences prediction are organized in clusters capturing similarities and differences in the dynamics of these sequences . Such performance corresponds to an unsupervised clustering of any spatio-temporal features and can be employed for time-dependent problems such as temporal segmentation , clustering of dynamic activity , self-supervised classification , action recognition , failure prediction , etc . We test and demonstrate the application of the embedding methodology to time-sequences of 3D human body poses . The MaxIn Algorithm for Maximizing Input Information . V.R. de Sa , Minimizing Disagreement for Self-Supervised Classification . S.N. Lindstaedt , Comparison of Two Unsupervised Neural Network Models for Redundancy Reduction . Masks which stand for the contour of objects provide a heuristic knowledge for the self-supervised learning pose estimation network . Pose estimation network has two fully connected layers and regards estimation as a self-supervised classification problem . Then , the pose can be transformed from value in pixel coordinates to actual value relative to the base coordinate of the manipulator . In this work we focus our attention on two types of public safety maps , namely road safety maps and crime maps . We formalize the problem of public safety mapping as a supervised image classification problem , in which a city-scale satellite map is treated as a set of satellite images each of each is assigned a safety label predicted using a model learned from training samples . To obtain this training data we leverage official police reports collected by police departments and released as open data . Also , the kappa coefficients for each of methods are as follows : ANN ( 0.88 ) , SVM ( 0.83 ) , and RF ( 0.80 ) . The results of this classification indicated that the extracted sample data from cognitive approach are reliable and help to semi-automation of supervised image classification process . The progress in supervised image classification research , has demonstrated the potential usefulness of incorporating fuzziness in the training , allocation and testing stages of several classification techniques . A multiresolution neural network approach to supervised classification is presented , exploiting the inherent fuzziness of such techniques in order to perform classification at different resolution levels and gain in computational complexity . Automatic image annotation is an important but highly challenging problem in semantic-based image retrieval . In this paper , we formulate image annotation as a supervised learning image classification problem under region-based image annotation framework . In region-based image annotation , keywords are usually associated with individual regions in the training data set . The authors describe the main features of a system supporting the selection of color palettes for qualitative data representation , such as in supervised or unsupervised image classification . Based on visual interaction , the system provides effective tools for browsing the Munsell color space and setting perceptual constraints on the colors , which it then selects automatically . paragraph : ABC There has also been work on learning summary statistics for Approximate Bayesian Computation by either learning to predict the parameters generating a sample as a supervised problem , or by using kernel embeddings as infinite dimensional summary statistics . See the work by kernel_bayes for an example of kernel - based approaches . section : Introduction Recent publications suggest that unsupervised pre - training of deep , hierarchical neural networks improves supervised pattern classification . Here we train such nets by simple online back - propagation , setting new , greatly improved records on MNIST , Latin letters , Chinese characters , traffic signs , NORB ( jittered , cluttered ) and CIFAR10 benchmarks . Early methods were based on stacked ( often denoising ) autoencoders or restricted Boltzmann machines [ reference ][ reference ][ reference ][ reference ] . A lot of promising recent work originates from the Skip - gram model [ reference ] , which inspired the skip - thought vectors [ reference ] and several techniques for unsupervised feature learning of images [ reference ] . Another intriguing line of work consists of the ladder network [ reference ] , which has achieved spectacular results on a semi - supervised variant of the MNIST dataset . It is designed to harness specialist expertise on classification of individual groups of organisms to form a ' catalogue of life ' of high taxonomic quality , and to solve problems of heterogeneity , scale and component database unreliability while providing a reconfigurable , flexible , cost-effective Internet online gateway to the distributed catalogue . This paper outlines our design and design rationale , explaining how our multi-tier federated approach makes maintenance of a consistent classification easier . We present the conceptual architecture and several intelligent agents prototyped to improve system performance with respect to scalability , stability and adaptability , and discuss the evaluation of this system . The component databases are heterogeneous , and are joined to the federation in various ways . We explain how our federated approach partitions the task of maintaining a consistent classification into manageable sub-tasks . We use both CORBA and XML and , while CORBA is widely used for interoperable systems and XML is attractive for data exchange , some problems have arisen in practice . Within a granular computing model of data mining , we reformulate the consistent classification problems . The granulation structures are partitions of a universe . These definitions are based on a unique framework : a parallel computation is defined as a partial order on the set of read and write operations invoked by processes , and a consistency criterion is defined as a constraint on this partial order . Such an approach provides a simple classification of consistency criteria , from the more to the less constrained one . This paper can also be considered as a survey on consistency criteria for DSMS.RésuméCet article présente un ensemble de critères The paper explains the evolvement of mobile database infrastructure;the classification of data consistency and SyncML infrastructure . Meanwhile it introducs heterogeneous data consistency in mobile database from multiple dimentions . Further , the concepts of energetic reasoning and shaving are analyzed and discussed . The other contribution is a classification of the consistency tests derived according to the domain reduction achieved . The particular strength of using consistency tests is based on their repeated application , so that the knowledge derived is propagated , i.e. , reused for acquiring additional knowledge . A taxonomy of CRs is then derived accordingly . Thus , this paper underlines the extended importance of consistency in B2Bis , furnishes a criterion for choosing B2Bi methods , helps classifying consistency support in B2B approaches and gives a starting point for finding consistency checking approaches . In this bipartite study we evaluate current accuracy and consistency problems using the KEGG database as a prominent example and propose design principles for dealing with such problems . In the first half , we present our computational approach for classifying inconsistencies and provide an overview of the classes of inconsistencies we identified . We detected inconsistencies both for database entries referring to substances and entries referring to reactions . SKICAT represents a system in which machine learning played a powerful and enabling role , and solved a difficult , scientifically significant problem . The primary benefits of our overall approach are increased data reduction throughput ; consistency of classification ; and the ability to easily access , analyze , and create new information from an otherwise unfathomable data set . Second , we implemented SCL by using the deductive database systems LDL thereby offering a useful toolset with which a prototype of a multilevel secure relational database application can efficiently be developed . Prototyping will help the database designer to arrive at a consistent data classification and at a satisfactory database design.<<ETX > > section : Proposed Method This section describes the proposed CORAL framework that addresses the problem of classifier inconsistency in ordinal regression CNNs based on multiple binary classification tasks for ranking . subsection : Preliminaries When the number of training data points is small , however , a network with high flexibility will quickly overfit the training data , resulting in a large model variance and a poor generalization performance . To address this problem , we propose a new ensemble learning method called InterBoost for small-sample image classification . In the training phase , InterBoost first randomly generates two complementary datasets to train two base networks of the same structure , separately , and then next two complementary datasets for further training the networks are generated through interaction ( or information sharing ) between the two base networks trained previously . The paper describes usage of deep neural network architectures such as VGG , ResNet and InceptionV3 for the classification of small images . Each image may contain one of four vehicle pose categories or background . Finally , a support vector machine ( SVM ) method is used to classify RCF defect in different depths . It implements a fast classification of small data . Compared with single features , fusion feature has the highest accuracy rate up to 98.73 % . Data with small sample essence is an important concept where the number of training samples is much less than the feature dimensions . The classification of small sample data has become a new problem in the fields of machine learning and pattern recognition . Ensemble learning is a kind of supervised learning process in which multiple learners are trained to solve the same problem . We also use a Naive Bayes classifier which returns an accuracy of 75 % . Our results underscore the need to extend popular classification techniques to sparse and small corpora , so as to perform hitherto neglected within genre classification and also exhibit that well known classifiers can also be employed in classifying ‘ small ’ data . However , few works have been carried out to solve small data problems ( like SAR ATR ) by Deep Learning method . In HRS , the deep structure and pattern classifier are combined to solve small data classification problems . After building the DBN with multiple Restricted Boltzmann Machines ( RBMs ) , hierarchical features can be obtained , and then they are fed to classifier directly . It is difficult to accumulate a large number of data with high quality in operational risk . Naive Bayes classifier is the one of best classifiers used to small data set classification . It is suitable for operational risk level prediction . Convolutional neural networks ( CNNs ) have shown remarkable results over the last several years for a wide range of computer vision tasks . A new architecture recently introduced by Sabour et al. , referred to as a capsule networks with dynamic routing , has shown great initial results for digit recognition and small image classification . The success of capsule networks lies in their ability to preserve more information about the input by replacing max-pooling layers with convolutional strides and dynamic routing , allowing for preservation of part-whole relationships in the data . Firstly , the feature extractor network is pre - trained for single - image face classification by temporarily ( just for this stage ) appending a classification FC layer on top of it , and training the network with the cross - entropy loss . Secondly , we train the whole network end - to - end for single - image classification with one - versus - all logistic regression loss , but exclude the ghost clusters from GhostVLAD because training images are not degraded in this stage . Finally , we add ghost clusters and enable image degradation , and train the whole network using image sets with one - versus - all logistic regression loss . Such warping is expensive and learning the transformation parameters is known difficult . STN has shown successes in small scale image classification problems . The inverse STN method replaces the expensive feature warping by efficient transformation parameter propagation .
Wavelet time – frequency analysis has been widely used for machinery fault diagnosis . Mechanical vibration signals can be converted to time – frequency images using wavelet transform , so machinery fault diagnosis can be transformed to the problem of image classification . Label consistent K-SVD algorithm has been proven to be effective in image classification , which incorporates a label consistent term namely discriminative sparse code error into the objective function .
In this study , we revisit their hypothesis , considering units with selectivity for lower-level features , and argue that selective units are not always harmful to the network performance . Specifically , by using DNNs trained for image classification , we analyzed the orientation selectivity of individual units . Orientation selectivity is a low-level selectivity widely studied in visual neuroscience , in which , when images of bars with several orientations are presented to the eye , many neurons in the visual cortex respond selectively to a specific orientation .
Instances are labeled as positive if the word is relevant for describing the visual content of the image , and negative otherwise . This novel approach of the image classification problem poses an alternative scenario where any text-image pair can be represented in such space , so any word could be considered for describing an image . The proposed methods are diverse and competitive , showing considerable improvements over the proposed baselines .
In this paper , we present a new approach that uses deep transfer learning techniques to recognize gestures based on the channel state information ( CSI ) extracted from WiFi signals . Firstly , the CSI streams of gestures are captured and the gesture segments are extracted based on the CSI amplitude changes , and then the WiFi-based gesture recognition problem is innovatively converted to an image classification problem by expressing CSI streams as an image matrix . After that , two deep transfer learning methods are applied to recognize gestures using high-level features extracted by deep convolutional neural network ( CNN ) and fine-tuned CNN models .
We propose a deep learning framework for image set classification with application to face recognition . An Adaptive Deep Network Template ( ADNT ) is defined whose parameters are initialized by performing unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines ( GRBMs ) .
The Riemannian kernel is shown to satisfy Mercer ’s theorem , so the CSPD matrix is symmetric and positive definite , and also lies on a Riemannian manifold . Test on three benchmark datasets shows that CSPD is both lower-dimensional and more discriminative data descriptor than standard SPD for the task of image set classification .
Image set classification finds its applications in a number of real-life scenarios such as classification from surveillance videos , multi-view camera networks and personal albums . Compared with single image based classification , it offers more promises and has therefore attracted significant research attention in recent years . Unlike many existing methods which assume images of a set to lie on a certain geometric surface , this paper introduces a deep learning framework which makes no such prior assumptions and can automatically discover the underlying geometric structure .
- An application to image data classification from Cherenkov telescope experiment . Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers .
A new classifier for image data classification named as linear twin bounded support tensor machine ( linear TBSTM ) is proposed by adding regularization terms in objective functions , which results in the realization of structural risk minimization avoids of the singularity of matrices . We know that up to now nonlinear classifiers based on STM for image data classification are not seen more . In order to remedy this limitation , a new matrix kernel function is introduced and based on which the nonlinear version of TBSTM is studied with a detailed theoretical derivation , and then a nonlinear classifier called as nonlinear TBSTM is suggested .
Third , perceiving information from different scales is essential for understanding parts as well as objects for tasks such as fine - grained classification and semantic segmentation . Thus , it is of critical importance to design good features for multi - scale stimuli for visual cognition tasks , including image classification , object detection , attention prediction , target tracking , action recognition , semantic segmentation , salient object detection . Unsurprisingly , multi - scale features have been widely used in both conventional feature design and deep learning .
It also augments the sampling locations in the convolution with offsets and learns the offsets via back - propagation end - to - end . It is shown effective on image classification tasks . Two crucial differences from deformable convolution make this work less general and adaptive .
The proposed LDMRBP operator is built using new neighborhood topology and new pattern encoding scheme . The adopted face recognition system consists of three stages : ( 1 ) face detection and alignment to normalize the input images to a common form if needed ; ( 2 ) feature extraction using the proposed descriptor in order to calculate the histogram , which represents the feature vector and ( 3 ) face recognition through a supervised image classification task using the simple K-Nearest Neighbors classifier . Simulated experiments on ORL , YALE and FERET under different illumination or facial expression conditions indicate that the proposed method outperforms other texture descriptors and other existing works of the literature .
It first implements a uniform target detector ( UTD ) to find a set of interfering signatures , I , then another uniform I-annihilated detector ( UIAD ) to find desired target signatures . The signatures in I are combined with the UIAD found desired signatures to form a target signatures matrix M which can be used for supervised image classification . In this case , OSP can be used for this purpose .
In particular , we demonstrate that embedding space projections of the decoder states of RNN Seq2Seq model trained on sequences prediction are organized in clusters capturing similarities and differences in the dynamics of these sequences . Such performance corresponds to an unsupervised clustering of any spatio-temporal features and can be employed for time-dependent problems such as temporal segmentation , clustering of dynamic activity , self-supervised classification , action recognition , failure prediction , etc . We test and demonstrate the application of the embedding methodology to time-sequences of 3D human body poses .
The MaxIn Algorithm for Maximizing Input Information . V.R. de Sa , Minimizing Disagreement for Self-Supervised Classification . S.N. Lindstaedt , Comparison of Two Unsupervised Neural Network Models for Redundancy Reduction .
Masks which stand for the contour of objects provide a heuristic knowledge for the self-supervised learning pose estimation network . Pose estimation network has two fully connected layers and regards estimation as a self-supervised classification problem . Then , the pose can be transformed from value in pixel coordinates to actual value relative to the base coordinate of the manipulator .
In this work we focus our attention on two types of public safety maps , namely road safety maps and crime maps . We formalize the problem of public safety mapping as a supervised image classification problem , in which a city-scale satellite map is treated as a set of satellite images each of each is assigned a safety label predicted using a model learned from training samples . To obtain this training data we leverage official police reports collected by police departments and released as open data .
Also , the kappa coefficients for each of methods are as follows : ANN ( 0.88 ) , SVM ( 0.83 ) , and RF ( 0.80 ) . The results of this classification indicated that the extracted sample data from cognitive approach are reliable and help to semi-automation of supervised image classification process .
The progress in supervised image classification research , has demonstrated the potential usefulness of incorporating fuzziness in the training , allocation and testing stages of several classification techniques . A multiresolution neural network approach to supervised classification is presented , exploiting the inherent fuzziness of such techniques in order to perform classification at different resolution levels and gain in computational complexity .
Automatic image annotation is an important but highly challenging problem in semantic-based image retrieval . In this paper , we formulate image annotation as a supervised learning image classification problem under region-based image annotation framework . In region-based image annotation , keywords are usually associated with individual regions in the training data set .
The authors describe the main features of a system supporting the selection of color palettes for qualitative data representation , such as in supervised or unsupervised image classification . Based on visual interaction , the system provides effective tools for browsing the Munsell color space and setting perceptual constraints on the colors , which it then selects automatically .
paragraph : ABC There has also been work on learning summary statistics for Approximate Bayesian Computation by either learning to predict the parameters generating a sample as a supervised problem , or by using kernel embeddings as infinite dimensional summary statistics . See the work by kernel_bayes for an example of kernel - based approaches .
section : Introduction Recent publications suggest that unsupervised pre - training of deep , hierarchical neural networks improves supervised pattern classification . Here we train such nets by simple online back - propagation , setting new , greatly improved records on MNIST , Latin letters , Chinese characters , traffic signs , NORB ( jittered , cluttered ) and CIFAR10 benchmarks .
Early methods were based on stacked ( often denoising ) autoencoders or restricted Boltzmann machines [ reference ][ reference ][ reference ][ reference ] . A lot of promising recent work originates from the Skip - gram model [ reference ] , which inspired the skip - thought vectors [ reference ] and several techniques for unsupervised feature learning of images [ reference ] . Another intriguing line of work consists of the ladder network [ reference ] , which has achieved spectacular results on a semi - supervised variant of the MNIST dataset .
It is designed to harness specialist expertise on classification of individual groups of organisms to form a ' catalogue of life ' of high taxonomic quality , and to solve problems of heterogeneity , scale and component database unreliability while providing a reconfigurable , flexible , cost-effective Internet online gateway to the distributed catalogue . This paper outlines our design and design rationale , explaining how our multi-tier federated approach makes maintenance of a consistent classification easier . We present the conceptual architecture and several intelligent agents prototyped to improve system performance with respect to scalability , stability and adaptability , and discuss the evaluation of this system .
The component databases are heterogeneous , and are joined to the federation in various ways . We explain how our federated approach partitions the task of maintaining a consistent classification into manageable sub-tasks . We use both CORBA and XML and , while CORBA is widely used for interoperable systems and XML is attractive for data exchange , some problems have arisen in practice .
Within a granular computing model of data mining , we reformulate the consistent classification problems . The granulation structures are partitions of a universe .
These definitions are based on a unique framework : a parallel computation is defined as a partial order on the set of read and write operations invoked by processes , and a consistency criterion is defined as a constraint on this partial order . Such an approach provides a simple classification of consistency criteria , from the more to the less constrained one . This paper can also be considered as a survey on consistency criteria for DSMS.RésuméCet article présente un ensemble de critères
The paper explains the evolvement of mobile database infrastructure;the classification of data consistency and SyncML infrastructure . Meanwhile it introducs heterogeneous data consistency in mobile database from multiple dimentions .
Further , the concepts of energetic reasoning and shaving are analyzed and discussed . The other contribution is a classification of the consistency tests derived according to the domain reduction achieved . The particular strength of using consistency tests is based on their repeated application , so that the knowledge derived is propagated , i.e. , reused for acquiring additional knowledge .
A taxonomy of CRs is then derived accordingly . Thus , this paper underlines the extended importance of consistency in B2Bis , furnishes a criterion for choosing B2Bi methods , helps classifying consistency support in B2B approaches and gives a starting point for finding consistency checking approaches .
In this bipartite study we evaluate current accuracy and consistency problems using the KEGG database as a prominent example and propose design principles for dealing with such problems . In the first half , we present our computational approach for classifying inconsistencies and provide an overview of the classes of inconsistencies we identified . We detected inconsistencies both for database entries referring to substances and entries referring to reactions .
SKICAT represents a system in which machine learning played a powerful and enabling role , and solved a difficult , scientifically significant problem . The primary benefits of our overall approach are increased data reduction throughput ; consistency of classification ; and the ability to easily access , analyze , and create new information from an otherwise unfathomable data set .
Second , we implemented SCL by using the deductive database systems LDL thereby offering a useful toolset with which a prototype of a multilevel secure relational database application can efficiently be developed . Prototyping will help the database designer to arrive at a consistent data classification and at a satisfactory database design.<<ETX > >
section : Proposed Method This section describes the proposed CORAL framework that addresses the problem of classifier inconsistency in ordinal regression CNNs based on multiple binary classification tasks for ranking . subsection : Preliminaries
When the number of training data points is small , however , a network with high flexibility will quickly overfit the training data , resulting in a large model variance and a poor generalization performance . To address this problem , we propose a new ensemble learning method called InterBoost for small-sample image classification . In the training phase , InterBoost first randomly generates two complementary datasets to train two base networks of the same structure , separately , and then next two complementary datasets for further training the networks are generated through interaction ( or information sharing ) between the two base networks trained previously .
The paper describes usage of deep neural network architectures such as VGG , ResNet and InceptionV3 for the classification of small images . Each image may contain one of four vehicle pose categories or background .
Finally , a support vector machine ( SVM ) method is used to classify RCF defect in different depths . It implements a fast classification of small data . Compared with single features , fusion feature has the highest accuracy rate up to 98.73 % .
Data with small sample essence is an important concept where the number of training samples is much less than the feature dimensions . The classification of small sample data has become a new problem in the fields of machine learning and pattern recognition . Ensemble learning is a kind of supervised learning process in which multiple learners are trained to solve the same problem .
We also use a Naive Bayes classifier which returns an accuracy of 75 % . Our results underscore the need to extend popular classification techniques to sparse and small corpora , so as to perform hitherto neglected within genre classification and also exhibit that well known classifiers can also be employed in classifying ‘ small ’ data .
However , few works have been carried out to solve small data problems ( like SAR ATR ) by Deep Learning method . In HRS , the deep structure and pattern classifier are combined to solve small data classification problems . After building the DBN with multiple Restricted Boltzmann Machines ( RBMs ) , hierarchical features can be obtained , and then they are fed to classifier directly .
It is difficult to accumulate a large number of data with high quality in operational risk . Naive Bayes classifier is the one of best classifiers used to small data set classification . It is suitable for operational risk level prediction .
Convolutional neural networks ( CNNs ) have shown remarkable results over the last several years for a wide range of computer vision tasks . A new architecture recently introduced by Sabour et al. , referred to as a capsule networks with dynamic routing , has shown great initial results for digit recognition and small image classification . The success of capsule networks lies in their ability to preserve more information about the input by replacing max-pooling layers with convolutional strides and dynamic routing , allowing for preservation of part-whole relationships in the data .
Firstly , the feature extractor network is pre - trained for single - image face classification by temporarily ( just for this stage ) appending a classification FC layer on top of it , and training the network with the cross - entropy loss . Secondly , we train the whole network end - to - end for single - image classification with one - versus - all logistic regression loss , but exclude the ghost clusters from GhostVLAD because training images are not degraded in this stage . Finally , we add ghost clusters and enable image degradation , and train the whole network using image sets with one - versus - all logistic regression loss .
Such warping is expensive and learning the transformation parameters is known difficult . STN has shown successes in small scale image classification problems . The inverse STN method replaces the expensive feature warping by efficient transformation parameter propagation .
Machinery fault diagnosis based on time–frequency images and label consistent K-SVD Causal importance of orientation selectivity for generalization in image recognition Image Annotation as Text-Image Matching: Challenge Design and Results Deep transfer learning for gesture recognition with WiFi signals Learning Non-linear Reconstruction Models for Image Set Classification Component SPD matrices: A low-dimensional discriminative data descriptor for image set classification Deep Reconstruction Models for Image Set Classification Ensembling Classifiers - An application to image data classification from Cherenkov telescope experiment TBSTM: A Novel and Fast Nonlinear Classification Method for Image Data Res2Net: A New Multi-scale Backbone Architecture Deformable Convolutional Networks Local Directional Multi Radius Binary Pattern - Novel Descriptor for Face Recognition Application A uniform projection-based unsupervised classification for hyperspectral imagery Clustering and Recognition of Spatiotemporal Features through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks. Proceedings of the 1993 Connectionist Models Summer School A Self-Supervised Learning Manipulator Grasping Approach Based on Instance Segmentation A Study on Public Safety Prediction using Satellite Imagery and Open Data [an abstract of dissertation and a summary of dissertation review] A new method for semi-automatic classification of remotely sensed images developed based on the cognitive approaches for producing spatial data required in geomatics applications Fuzzy image classification using multiresolution neural networks with applications to remote sensing Image Annotation Using the SimpleDecisionTree A system for the automatic selection of conspicuous color sets for qualitative data display Towards a Neural Statistician Multi-column deep neural networks for image classification InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Design and performance evaluation of a web-based multi-tier federated system for a catalogue of life Experiences with a Hybrid Implementation of a Globally Distributed Federated Database System Granular Computing as a Basis for Consistent Classification Problems A suite of definitions for consistency criteria in distributed shared memories A Research into Isomerous Data Consistency in Mobile DataBase Constraint propagation techniques for the disjunctive scheduling problem Taxonomy on Consistency Requirements in the Business Process Integration Context Towards rule-based metabolic databases: a requirement analysis based on KEGG Automated analysis of a large-scale sky survey: the SKICAT system The deductive filter approach to MLS database prototyping Consistent Rank Logits for Ordinal Regression with Convolutional Neural Networks Interactive Boosting of Neural Networks for Small-sample Image Classification Deep Learning in Vehicle Pose Recognition on Two-Dimensional Images Fast classification for rail defect depths using a hybrid intelligent method Multi-Label Classification of Small Samples Using an Ensemble Technique Bundeli Folk-Song Genre Classification with kNN and SVM Hierarchical Recognition System for Target Recognition from Sparse Representations Naive Bayes method in operational risk level prediction Capsules for Object Segmentation GhostVLAD for set-based face recognition Deformable Convolutional Networks
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CVPR IEEE transactions on pattern analysis and machine intelligence 2017 IEEE International Conference on Computer Vision (ICCV) IGARSS ICLR 2012 IEEE Conference on Computer Vision and Pattern Recognition NIPS WIDM WAIM SEKE ACSAC ArXiv ICON ACCV 2017 IEEE International Conference on Computer Vision (ICCV)
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