LEADER 13051nam 22009135 450 001 996466222003316 005 20230329233647.0 010 $a3-319-26532-6 024 7 $a10.1007/978-3-319-26532-2 035 $a(CKB)4340000000001213 035 $a(SSID)ssj0001585360 035 $a(PQKBManifestationID)16264033 035 $a(PQKBTitleCode)TC0001585360 035 $a(PQKBWorkID)14864434 035 $a(PQKB)10507883 035 $a(DE-He213)978-3-319-26532-2 035 $a(MiAaPQ)EBC6296208 035 $a(MiAaPQ)EBC5591603 035 $a(Au-PeEL)EBL5591603 035 $a(OCoLC)960152586 035 $a(PPN)190529547 035 $a(EXLCZ)994340000000001213 100 $a20151121d2015 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aNeural Information Processing$b[electronic resource] $e22nd International Conference, ICONIP 2015, Istanbul, Turkey, November 9-12, 2015, Proceedings, Part I /$fedited by Sabri Arik, Tingwen Huang, Weng Kin Lai, Qingshan Liu 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (XVII, 742 p. 251 illus. in color.) 225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v9489 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-319-26531-8 327 $aIntro -- Preface -- Organization -- Contents - Part I -- Texture Classification with Patch Autocorrelation Features -- 1 Introduction -- 2 Related Work -- 3 Translation and Rotation Invariant Patch Autocorrelation Features -- 3.1 Texture Features -- 4 Texture Classification Experiments -- 4.1 Data Set -- 4.2 Learning Methods -- 4.3 Implementation and Evaluation -- 4.4 Parameter Tuning -- 4.5 Results on Brodatz Data Set -- 5 Conclusion -- References -- Novel Architecture for Cellular Neural Network Suitable for High-Density Integration of Electron Dev ... -- Abstract -- 1 Introduction -- 2 Device Architecture -- 2.1 Neuron -- 2.2 Synapse -- 2.3 Network -- 3 Learning Principle -- 4 Fabrication Process -- 5 Experimental Result -- 6 Conclusion -- References -- Analyzing the Impact of Feature Drifts in Streaming Learning -- 1 Introduction -- 2 Problem Statement -- 3 Simulating Feature Drifts -- 4 Analysis -- 4.1 Evaluated Algorithms -- 4.2 Experimental Protocol -- 4.3 Results Obtained -- 5 Conclusion -- References -- Non-linear Metric Learning Using Metric Tensor -- Abstract -- 1 Introduction -- 2 Theoretical Analysis -- 3 Problem Simplification -- 4 Algorithm -- 5 Experiment -- 5.1 Performance in Supervised Metric Learning -- 5.2 Application in Semi-supervised Clustering -- 6 Conclusion -- References -- An Optimized Second Order Stochastic Learning Algorithm for Neural Network Training -- 1 Introduction -- 2 Proposed Algorithm -- 2.1 Overview of Learning Algorithms -- 2.2 Stochastic Diagonal Levenberg-Marquardt Algorithm -- 2.3 Bounded SDLM Algorithm -- 3 Experimental Design -- 4 Results and Discussions -- 5 Conclusion and Future Works -- References -- Max-Pooling Dropout for Regularization of Convolutional Neural Networks -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Max-Pooling Dropout -- 3.1 Max-Pooling Dropout at Training Time. 327 $a3.2 Probabilistic Weighted Pooling at Test Time -- 4 Empirical Evaluations -- 4.1 Probabilistic Weighted Pooling vs. (Scaled) Max-Pooling -- 4.2 Max-Pooling Dropout vs. Stochastic Pooling -- 5 Conclusions -- References -- Predicting Box Office Receipts of Movies with Pruned Random Forest -- 1 Introduction -- 2 Methodology -- 2.1 Movie Information Data Collection -- 2.2 Pruned Random Forest -- 2.3 Advice for Screen Schedule -- 3 Results -- 3.1 The Classification Performance of Pruned Random Forest -- 3.2 Comparison with Other Models -- 4 Conclusion -- References -- A Novel 1-graph Based Image Classification Algorithm -- 1 Introduction -- 2 Background -- 2.1 Sparse Representation Based Classification Algorithm -- 2.2 1-Graph -- 3 1-graph Based Image Classification Method -- 3.1 Relationship Between Training Samples and Classes -- 3.2 Classification Process -- 4 Experiment Results -- 4.1 Face Recognition -- 4.2 Handwritten Digit Recognition -- 5 Conclusion and Future Work -- References -- Classification of Keystroke Patterns for User Identification in a Pressure-Based Typing Biometrics S ... -- Abstract -- 1 Introduction -- 2 System Design -- 2.1 Force Sensor -- 2.2 Microprocessor Design with Arduino -- 3 Classification -- 3.1 Particle Swarm Optimization -- 3.2 K-Means -- 4 Experimental Setup and Results -- 5 Conclusions -- References -- Discriminative Orthonormal Dictionary Learning for Fast Low-Rank Representation -- 1 Introduction -- 2 Discriminative Orthonormal Dictionary Learning -- 2.1 Formulation -- 2.2 Optimization -- 3 Fast Low-Rank Representation -- 4 Experiments -- 4.1 Extended Yale B Dataset -- 4.2 AR Dataset -- 4.3 Caltech 101 Dataset -- 5 Conclusions -- References -- Supervised Topic Classification for Modeling a Hierarchical Conference Structure -- 1 Introduction -- 2 Supervised Classification, Flat Case -- 3 Topics Hierarchy. 327 $a4 Empirical Results -- 5 Conclusion -- References -- A Framework for Online Inter-subjects Classification in Endogenous Brain-Computer Interfaces -- Abstract -- 1 Introduction -- 2 Methods -- 2.1 Base Classifiers' Weights Initialization -- 2.2 Base Classifiers' Weights Adaptation Using Ensemble Predictions -- 2.3 Base Classifiers' Weights Adaptation Using Ensemble Predictions Reinforced by Interaction Error-Related Potentials -- 3 Experiments -- 3.1 EEG Data Sets -- 3.2 Procedure for Simulating IErrPs -- 3.3 Results -- 4 Conclusion -- References -- A Bayesian Sarsa Learning Algorithm with Bandit-Based Method -- 1 Introduction -- 2 Bayesian Sarsa -- 2.1 Q-values Distribution -- 2.2 Updating Q-Values -- 2.3 Actions Selection -- 3 Experimental Results -- 3.1 Gridworld -- 4 Conclusion -- References -- Incrementally Built Dictionary Learning for Sparse Representation -- 1 Introduction -- 2 Background on Dictionary Learning -- 3 Incrementally Built Dictionary Learning -- 3.1 Approach Description -- 3.2 Incremental Learning Rule -- 3.3 Sparse Coding-Based Feature Extraction -- 4 Experimentations -- 4.1 Digits Recognition -- 4.2 Face Recognition -- 4.3 The Effects of Incremental Learning -- 5 Conclusion -- References -- Learning to Reconstruct 3D Structure from Object Motion -- 1 Introduction -- 2 Related Work -- 3 DNN Based 3D Reconstruction Method -- 3.1 Reconstruction Unit -- 3.2 Deep Neural Network for 3D Reconstruction -- 3.3 Temporal Integration -- 4 Experiments -- 4.1 Data Generation -- 4.2 Reconstruction on Synthetic Images -- 4.3 Reconstruction on Real Images -- 5 Conclusions -- References -- Convolutional Networks Based Edge Detector Learned via Contrast Sensitivity Function -- 1 Introduction -- 2 The Model Architecture -- 2.1 Convolutional Networks -- 2.2 Multi-channel Structure -- 3 Training Data Generation and Annotation. 327 $a3.1 Training Data Generation -- 3.2 Training Data Annotation -- 4 Experiments -- 5 Conclusion -- References -- Learning Algorithms and Frame Signatures for Video Similarity Ranking -- 1 Introduction -- 2 Similar-Video Retrieval -- 2.1 Frame Features -- 2.2 Clustering Algorithms for Exemplar Extraction -- 2.3 Global and Local Alignments -- 3 Video Signature Tools -- 3.1 Frame Signature -- 3.2 Word and Bag of Words -- 4 Experiments on Video Similarity Ranking -- 4.1 Test Video Set and Evaluation Method -- 4.2 Experimental Results -- 5 Discussions -- References -- On Measuring the Complexity of Classification Problems -- 1 Introduction -- 2 Complexity Measures/Indices -- 2.1 Feature/Attribute Overlapping -- 2.2 Separability of Classes -- 2.3 Geometry, Topology and Density -- 3 Conclusion -- References -- The Effect of Stemming and Stop-Word-Removal on Automatic Text Classification in Turkish Language -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Proposed Work -- 4 Methodology for Dataset -- 5 The Experimental Results -- 6 Conclusion -- References -- Example-Specific Density Based Matching Kernel for Classification of Varying Length Patterns of Speech Using Support Vector Machines -- 1 Introduction -- 2 Dynamic Kernels for Sets of Feature Vectors -- 3 Example-Specific Density Based Matching Kernel for Sets of Feature Vectors -- 4 Experimental Studies on Speech Emotion Recognition and Speaker Identification -- 5 Conclusions -- References -- Possibilistic Information Retrieval Model Based on Relevant Annotations and Expanded Classification -- Abstract -- 1 Introduction -- 2 Related Works -- 3 Filtering Annotation Approach -- 4 Classification of Annotations -- 4.1 Initial Classification -- 4.2 Clusters Extension -- 5 Experimental Evaluation and Analysis of Results -- 5.1 Used Collection of Data -- 5.2 Effects of the Classified and Filtered Annotation. 327 $a6 Conclusion and Future Works -- References -- A Transfer Learning Method with Deep Convolutional Neural Network for Diffuse Lung Disease Classification -- 1 Introduction -- 2 Methods -- 2.1 Deep Convolutional Neural Network (DCNN) -- 2.2 Transfer Learning for DCNN -- 2.3 Materials -- 3 Results -- 4 Summary and Discussion -- References -- Evaluation of Machine Learning Algorithms for Automatic Modulation Recognition -- Abstract -- 1 Introduction -- 2 System Model, Signal and Channel Representation -- 3 Feature Extraction -- 3.1 Spectral Features -- 3.2 Statistical Features -- 4 Nonnegative Matrix Factorization (NMF) -- 5 Experimental Results -- 6 Conclusion -- References -- Probabilistic Prediction in Multiclass Classification Derived for Flexible Text-Prompted Speaker Verification -- 1 Introduction -- 2 Probabilistic Prediction for Text-Prompted Speaker Verification -- 2.1 Multistep Speaker and Text Verification Using GEBI -- 2.2 Probabilistic Prediction for Speaker and Text Verification -- 2.3 Loss Functions for Evaluating the Performance -- 3 Experiments -- 3.1 Experimental Setting -- 3.2 Experimental Results and Analysis -- 4 Conclusion -- References -- Simple Feature Quantities for Learning of Dynamic Binary Neural Networks -- 1 Introduction -- 2 Dynamic Binary Neural Networks -- 3 Teacher Signal and Feature Quantities -- 4 Greedy Search Based Sparsification Algorithm -- 5 Conclusions -- References -- Transfer Metric Learning for Kinship Verification with Locality-Constrained Sparse Features -- 1 Introduction -- 2 Proposed Approach -- 2.1 Feature Extraction -- 2.2 NRTML -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Experimental Results -- 4 Conclusion -- References -- Unsupervised Land Classification by Self-organizing Map Utilizing the Ensemble Variance Information in Satellite-Borne Polarimetric Synthetic Aperture Radar. 327 $a1 Introduction. 330 $aThe four volume set LNCS 9489, LNCS 9490, LNCS 9491, and LNCS 9492 constitutes the proceedings of the 22nd International Conference on Neural Information Processing, ICONIP 2015, held in Istanbul, Turkey, in November 2015. The 231 full papers presented were carefully reviewed and selected from 375 submissions. The 4 volumes represent topical sections containing articles on Learning Algorithms and Classification Systems; Artificial Intelligence and Neural Networks: Theory, Design, and Applications; Image and Signal Processing; and Intelligent Social Networks. 410 0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v9489 606 $aPattern recognition systems 606 $aComputer vision 606 $aArtificial intelligence 606 $aComputer science 606 $aData mining 606 $aApplication software 606 $aAutomated Pattern Recognition 606 $aComputer Vision 606 $aArtificial Intelligence 606 $aTheory of Computation 606 $aData Mining and Knowledge Discovery 606 $aComputer and Information Systems Applications 615 0$aPattern recognition systems. 615 0$aComputer vision. 615 0$aArtificial intelligence. 615 0$aComputer science. 615 0$aData mining. 615 0$aApplication software. 615 14$aAutomated Pattern Recognition. 615 24$aComputer Vision. 615 24$aArtificial Intelligence. 615 24$aTheory of Computation. 615 24$aData Mining and Knowledge Discovery. 615 24$aComputer and Information Systems Applications. 676 $a006.3 702 $aArik$b Sabri$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHuang$b Tingwen$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLai$b Weng Kin$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLiu$b Qingshan$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996466222003316 996 $aNeural Information Processing$92554499 997 $aUNISA