Holdings: Neural networks and statistical learning

Neural networks and statistical learning:

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Bibliographic Details
Main Authors:	Du, Ke-Lin (Author), Swamy, M. N. S. (Author)
Format:	Book
Language:	English
Published:	London Springer 2014
Subjects:	Ingenieurwissenschaften Engineering Data mining Optical pattern recognition Maschinelles Lernen Neuronales Netz
Online Access:	Inhaltsverzeichnis
Physical Description:	XXVII, 824 S. Ill. : graph. Darst.
ISBN:	9781447155706

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adam_text	Contents 1 Introduction..................................................... 1.1 Major Events in Neural Networks Research.................. 1.2 Neurons................................................... 1.2.1 The McCulloch-Pitts Neuron Model................. 1.2.2 Spiking Neuron Models............................ 1.3 Neural Networks.......................................... . 1.4 Scope of the Book........................................ References....................................................... 2 Fundamentals of Machine Learning................................. 2.1 Learning Methods......................................... 2.2 Learning and Generalization.............................. 2.2.1 Generalization Error............................. 2.2.2 Generalization by Stopping Criterion............. 2.2.3 Generalization by Regularization................. 2.2.4 Fault Tolerance and Generalization.......... 2.2.5 Sparsity Versus Stability................... 2.3 Model Selection........................................... 2.3.1 Crossvalidation............................. 2.3.2 Complexity Criteria......................... 2.4 Bias and Variance................................... 2.5 Robust Learning........................................... 2.6 Neural Network Processors................................. 2.7 Criterion Functions.................................. 2.8 Computational Learning Theory............................. 2.8.1 Vapnik-Chervonenkis Dimension..................... 2.8.2 Empirical Risk-Minimization Principle............ 2.8.3 Probably Approximately Correct Learning.......... . 2.9 No-Free~Lunch Theorem..................................... 2.10 Neural Networks as Universal Machines .................... 2.10.1 Boolean Function Approximation................... 2.10.2 Linear Separability and Nonlinear Separability . . . . 2.10.3 Continuous Function Approximation................ 2.10.4 Winner-Takes-All......................... 1 1 3 5 6 8 12 13 15 15 19 21 21 23 24 25 25 26 28 29 31 33 36 39 40 41 43 44 45 45 47 49 50 xi tents 51 51 53 54 55 59 67 67 68 69 71 74 76 79 83 83 84 85 90 95 96 96 99 101 102 102 104 108 110 112 113 115 115 119 127 127 128 129 130 2.11 Compressed Sensing and Sparse Approximation.... 2.11.1 Compressed Sensing...................... 2.11.2 Sparse Approximation.................... 2.11.3 LASSO and Greedy Pursuit................ 2.12 Bibliographical Notes............................... References............................................... Perceptrons.............................................. 3.1 One-Neuron Perceptron............................. 3.2 Single-Layer Perceptron......................... 3.3 Perceptron Learning Algorithm..................... 3.4 Least-Mean Squares (LMS) Algorithm................ 3.5 P-Delta Rule...................................... 3.6 Other Learning Algorithms......................... References............................................... Multilayer Perceptrons: Architecture and Error Backpropagation ............................... 4.1 Introduction...................................... 4.2 Universal Approximation........................... 4.3 Backpropagation Learning Algorithm................ 4.4 Incremental Learning Versus Batch Learning........ 4.5 Activation Functions for the Output Layer......... 4.6 Optimizing Network Structure...................... 4.6.1 Network Pruning Using Sensitivity Analysis 4.6.2 Network Pruning Using Regularization . . . 4.6.3 Network Growing......................... 4.7 Speeding Up Learning Process...................... 4.7.1 Eliminating Premature Saturation....... 4.7.2 Adapting Learning Parameters........... 4.7.3 Initializing Weights................... 4.7.4 Adapting Activation Function........... 4.8 Some Improved BP Algorithms....................... 4.8.1 BP with Global Descent.................. 4.8.2 Robust BP Algorithms................... 4.9 Resilient Propagation (RProp)..................... References............................................... Multilayer Perceptrons: Other Learning Techniques........ 5.1 Introduction to Second-Order Learning Methods..... 5.2 Newton’s Methods.................................. 5.2.1 Gauss-Newton Method..................... 5.2.2 Levenberg-Marquardt Method............. Contents xiii 5.3 Quasi-Newton Methods....................................... 133 5.3.1 BFGS Method....................................... 134 5.3.2 One-Step Secant Method............................ 136 5.4 Conjugate-Gradient Methods................................. 136 5.5 Extended Kalman Filtering Methods.......................... 141 5.6 Recursive Least Squares.................................... 143 5.7 Natural-Gradient Descent Method............................ 144 5.8 Other Learning Algorithms.................................. 145 5.8.1 Layerwise Linear Learning......................... 145 5.9 Escaping Local Minima...................................... 146 5.10 Complex-Valued MLPs and Their Learning..................... 147 5.10.1 Split Complex BP.................................. 148 5.10.2 Fully Complex BP.................................. 148 References........................................................ 152 6 Hopfield Networks, Simulated Annealing? and Chaotic Neural Networks.................................... 159 6.1 Hopfield Model............................................ 159 6.2 Continuous-Time Hopfield Network.......................... 162 6.3 Simulated Annealing........................................ 165 6.4 Hopfield Networks for Optimization....................... 168 6.4.1 Combinatorial Optimization Problems............... 169 6.4.2 Escaping Local Minima for Combinatorial Optimization Problems............................. 172 6.4.3 Solving Other Optimization Problems............... 173 6.5 Chaos and Chaotic Neural Networks.......................... 175 6.5.1 Chaos, Bifurcation, and Fractals.................. 175 6.5.2 Chaotic Neural Networks........................... 176 6.6 Multistate Hopfield Networks............................... 179 6.7 Cellular Neural Networks................................... 180 References.................................................. 183 7 * 7 Associative Memory Networks ...................................... 187 7.1 Introduction............................................... 187 7.2 Hopfield Model: Storage and Retrieval...................... 189 7.2.1 Generalized Hebbian Rule ........................ 189 7.2.2 Pseudoinverse Rule............................. 191 7.2.3 Perceptron-Type Learning Rule..................... 191 7.2.4 Retrieval Stage................................ 192 7.3 Storage Capability of the Hopfield Model ............... 193 7.4 Increasing Storage Capacity. . .......................... 197 7.5 Multistate Hopfield Networks for Associative Memory ..... 200 7.6 Multilayer Perceptrons as Associative Memories ........... 201 7.7 Hamming Network............................................ 203 xiv Contents 7.8 Bidirectional Associative Memories........................... 205 7.9 Cohen֊Grossberg Model........................................ 206 7.10 Cellular Networks............................................ 207 References........................................................ 21.1 8 Clustering I: Basic Clustering Models and Algorithms................. 215 8.1 Introduction................................................. 215 8.1.1 Vector Quantization................................. 215 8.1.2 Competitive Learning................................ 217 8.2 Self-Organizing Maps....................................... 218 8.2.1 Kohonen Network..................................... 220 8.2.2 Basic Self-Organizing Maps.......................... 221 8.3 Learning Vector Quantization................................. 228 8.4 Nearest-Neighbor Algorithms.................................. 231 8.5 Neural Gas................................................... 234 8.6 ART Networks................................................. 237 8.6.1 ART Models.......................................... 238 8.6.2 ART J............................................... 239 8.7 С-Means Clustering........................................... 241 8.8 Subtractive Clustering....................................... 244 8.9 Fuzzy Clustering............................................. 247 8.9.1 Fuzzy С-Means Clustering............................ 247 8.9.2 Other Fuzzy Clustering Algorithms................... 250 References......................................................... 253 9 9 Clustering II: Topics in Clustering.................................. 259 9.1 The Underutilization Problem............................... 259 9.1.1 Competitive Learning with Conscience............... 259 9.1.2 Rival Penalized Competitive Learning............... 261 9.1.3 Softcompetitive Learning........................... 263 9.2 Robust Clustering............................................ 264 9.2Л Possibilistic C-Means.............................. 266 9.2.2 A Unified Framework for Robust Clustering........ 267 9.3 Supervised Clustering...................................... 268 9.4 Clustering Using Non-Euclidean Distance Measures............. 269 9.5 Partitional, Hierarchical, and Density-Based Clustering... 271 9.6 Hierarchical Clustering...................................... 272 9.6.1 Distance Measures, Cluster Representations, and Dendrograms.................................... 272 9.6.2 Minimum Spanning Tree (MST) Clustering............. 274 9.6.3 BIRCH, CURE, CHAMELEON, and DBSCAN . . . 276 9.6.4 Hybrid Hierarchical/Partitional Clustering......... 279 Contents XV 9.7 Constructive Clustering .Techniques........................... 280 9.8 Cluster Validity.............................................. 282 9.8.1 Measures Based on Compactness and Separation of Clusters.......................................... 282 9.8.2 Measures Based on Hypervolume and Density of Clusters.......................................... 284 9.8.3 Crisp Silhouette and Fuzzy Silhouette............. 285 9.9 Projected Clustering.......................................... 286 9.10 Spectral Clustering........................................... 288 9.11 Coclustering.................................................. 289 9.12 Handling Qualitative Data..................................... 289 9.13 Bibliographical Notes..................................... . 290 References......................................................... 291 10 Radial 10.1 10.2 10.3 10.4 10.5 10.6 10.7 10.8 10.9 Basis Function. Networks.................................. Introduction ............................................. 10.1.1 RBF Network Architecture............... 10.1.2 Universal Approximation of RBF Networks ...... 10.1.3 RBF Networks and Classification .............. . . 10.1.4 Learning for RBF Networks........................ Radial Basis Functions.................................... Learning RBF Centers...................................... Learning the Weights...................................... 10.4.1 Least-Squares Methods for Weight Learning . . . . . RBF Network Learning Using Orthogonal Least-Squares. . . . 10.5.1 Batch Orthogonal Least-Squares................... 10.5.2 Recursive Orthogonal Least-Squares............... Supervised Learning of All Parameters .................... 10.6.1 Supervised Learning for General RBF Networks..................................... 10.6.2 Supervised Learning for Gaussian RBF Networks..................................... 10.6.3 Discussion on Supervised Learning................ 10.6.4 Extreme Learning Machines........................ Various Learning Methods.................................. Normalized RBF Networks................... Optimizing Network Structure.............................. 10.9.1 Constructive Methods................... 10.9.2 Resource-Allocating Networks........... 10.9.3 Pruning Methods................ 10.10 Complex RBF Networks.............. 10.11 A Comparision of RBF Networks and MLPs........... 10.12 Bibliographical Notes............................ References.............................................. 299 299 300 301 302 302 303 306 308 308 310 310 312 313 313 314 316 316 317 319 320 320 322 324 324 326 328 330 XVI Contents 11 Recurrent Neural Networks........................................ 337 11.1 Introduction............................................... 337 11.2 Fully Connected Recurrent Networks......................... 339 11.3 Time-Delay Neural Networks................................. 340 11.4 Backpropagation for Temporal Learning...................... 342 11.5 RBF Networks for Modeling Dynamic Systems.................. 345 11.6 Some Recurrent Models...................................... 346 11.7 Reservoir Computing........................................ 348 References........................................................ 351 12 Principal Component Analysis....................................... 355 12.1 Introduction............................................... 355 12.1.1 Hebbian Learning Rule............................. 356 12.1.2 Oja’s Learning Rule............................... 357 12.2 PC A: Conception and Model................................. 358 12.2.1 Factor Analysis................................... 361 12.3 Hebbian Rule-Based PC A.................................... 362 12.3.1 Subspace Learning Algorithms...................... 362 12.3.2 Generalized Hebbian Algorithm..................... 366 12.4 Least Mean Squared Error-Based PCA......................... 368 12.4.1 Other Optimization-Based PCA...................... 371 12.5 Anti-Hebbian Rule-Based PCA................................ 372 12.5.1 APEX Algorithm.................................... 374 12.6 Nonlinear PCA.............................................. 378 12.6.1 Autoassociative Network-Based Nonlinear PCA . . . 379 12.7 Minor Component Analysis................................... 380 12.7.1 Extracting the First Minor Component.............. 380 12.7.2 Self-Stabilizing Minor Component Analysis...... 381 12.7.3 Oja-Based MCA..................................... 382 12.7.4 Other Algorithms.................................. 383 12.8 Constrained PCA............................................ 383 12.8.1 Sparse PCA........................................ 385 12.9 Localized PCA, Incremental PCA, and Supervised PCA .... 386 12.10 Complex-Valued PCA......................................... 387 12.11 Two-Dimensional PCA...................................... 388 12.12 Generalized Eigenvalue Decomposition....................... 390 12.13 Singular Value Decomposition............................. 391 12.13.1 Crosscorrelation Asymmetric PCA Networks...... 391 12.13.2 Extracting Principal Singular Components for Nonsquare Matrices............................ 394 12.13.3 Extracting Multiple Principal Singular Components....................................... 395 12.14 Canonical Correlation Analysis............................. 396 References........................................................ 399 Contents xvii 13 Nonnegative Matrix Factorization................................... 407 13.1 introduction............................................... 407 13.2 Algorithms for NMF......................................... 408 13.2.1 Multiplicative Update Algorithm and Alternating Nonnegative Least Squares....... 409 13.3 Other NMF Methods.......................................... 411 13.3.1 NMF Methods for Clustering........................ 414 References...................................................... 415 14 Independent Component Analysis............................... 419 14.1 Introduction............................................... 419 14.2 ICA Model.................................................. 420 14.3 Approaches to ICA.......................................... 421 14.4 Popular ICA Algorithms..................................... 424 14.4.1 Infomax ICA...................................... 424 14.4.2 EASI, JADE, and Natural-Gradient ICA.............. 425 14.4.3 FastlCA Algorithm................................. 426 14.5 ICA Networks............................................... 431 14.6 Some ICA Methods...................................... 434 14.6.1 Nonlinear ICA..................................... 434 14.6.2 Constrained ICA................................... 434 14.6.3 Nonnegativity ICA................................. 435 14.6.4 ICA for Convolutive Mixtures...................... 436 14.6.5 Other Methods..................................... 437 14.7 Complex-Valued ICA......................................... 439 14.8 Stationary Subspace Analysis and Slow Feature Analysis ... 441 14.9 EEG, MEG and fMRI.......................................... 442 References................................................... 446 15 * 15 Discriminant Analysis............................ 15.1 Linear Discriminant Analysis.............. 15.1.1 Solving Small Sample Size Problem 15.2 Fisherfaces............................... 15.3 Regularized LDA........................... 15.4 Uncorrelated LDA and Orthogonal LDA. . . . 15.5 LDA/GSVD and LDA/OR....................... 15.6 Incremental LDA................... 15.7 Other Discriminant Methods ............... 15.0 Nonlinear Discriminant Analysis ......... 15.9 Two-Dimensional Discriminant Analysis . . . . References....................................... 451 451 454 455 456 457 459 460 46/ 464 465 Contents xviii 16. Support Vector Machines............................................ 469 16.1 Introduction................................................ 469 16.2 SVM Model................................................... 472 16.3 Solving the Quadratic Programming Problem................... 475 16.3.1 Chunking........................................... 476 16.3.2 Decomposition...................................... 476 16.3.3 Convergence of Decomposition Methods.............. 480 16.4 Least-Squares SVMs.......................................... 481 16.5 SVM Training Methods...................................... 484 16.5.1 SVM Algorithms with Reduced Kernel Matrix. . . , 484 16.5.2 v-SVM.T............................................ 485 16.5.3 Cutting-Plane Technique............................ 486 16.5.4 Gradient-Based Methods............................. 487 16.5.5 Training SVM in the Primal Formulation............. 488 16.5.6 Clustering-Based SVM .............................. 489 16.5.7 Other Methods...................................... 490 16.6 Pruning SVMs................................................ 493 16.7 Multiclass SVMs............................................ 495 16.8 Support Vector Regression................................... 497 16.9 Support Vector Clustering................................... 502 16.10 Distributed and Parallel SVMs............................... 504 16.11 SVMs for One-Class Classification........................... 506 16.12 Incremental SVMs............................................ 507 16.13 SVMs for Active, Transductive, and Semi-Supervised Learning.................................................... 509 16.13.1 SVMs for Active Learning........................... 509 16.13.2 SVMs for Transductive or Semi-Supervised Learning........................................... 509 16.14 Probabilistic Approach to SVM............................... 512 16.14.1 Relevance Vector Machines.......................... 513 References...................................................... 514 17 17 Other Kernel Methods............................................ 525 17.1 Introduction............................................... 525 17.2 Kernel PC A................................................ 527 17.3 Kernel LDA................................................. 531 17.4 Kernel Clustering......................................... 533 17.5 Kernel Autoassociators, Kernel CCA and Kernel ÏCA.......... 534 17.6 Other Kernel Methods....................................... 536 17.7 Multiple Kernel Learning................................... 537 References......................................................... 540 Contents XIX 18 Reinforcement Learning.............................................. 547 18.1 Introduction................................................ 547 18.2 Learning Through Awards..................................... 549 18.3 Actor-Critic Model.......................................... 551 18.4 Model-Free and Model-Based Reinforcement Learning........ 552 18.5 Temporal-Difference Learning................................ 554 18.6 ^֊Learning.................................................. 556 18.7 Learning Automata........................................... 558 References....................................................... 560 19 Probabilistic and Bayesian Networks................................. 563 19.1 Introduction................................................ 563 19.1.3 Classical Versus Bayesian Approach................ 564 19.1.2 Bayes’ Theorem.................................. 565 19.1.3 Graphical Models.................................. 566 19.2 Bayesian Network Model...................................... 567 19.3 Learning Bayesian Networks.................................. 570 19.3.1 Learning the Structure............................ 570 19.3.2 Learning the Parameters. .......................... 575 19.3.3 Constraint-Handling................................ 577 19.4 Bayesian Network Inference.................................. 577 19.4.1 Belief Propagation................................. 578 19.4.2 Factor Graphs and the Belief Propagation Algorithm.......................................... 580 19.5 Sampling (Monte Carlo) Methods.............................. 583 19.5.1 Gibbs Sampling..................................... 585 19.6 Variational Bayesian Methods................................ 586 19.7 Hidden Markov Models........................................ 588 19.8 Dynamic Bayesian Networks................................... 591 19.9 Expectation-Maximization Algorithm.......................... 592 19.10 Mixture Models.............................................. 594 19.10.1 Probabilistic PCA.................................. 595 19.10.2 Probabilistic Clustering........................... 596 19.10.3 Probabilistic ICA.................................. 597 19.11 Bayesian Approach to Neural Network Learning................ 599 19.12 Boltzmann Machines......................................... 601 19.12.1 Boltzmann Learning Algorithm. .............. 602 19.12.2 Mean֊Field-Theory Machine......................... 604 19.12.3 Stochastic Hopfield Networks...................... 605 19.13 Training Deep Networks .................................... 606 References..................................................... 610 [tents 621 621 622 623 624 625 628 629 630 632 633 633 634 637 640 645 645 646 652 653 655 657 658 659 659 661 662 663 664 665 665 667 668 669 670 671 675 677 677 678 Combining Multiple Learners: Data Fusion and Emsemble Learning................................................. 20.1 Introduction...................................... 20.1.1 Ensemble Learning Methods................ 20.1.2 Aggregation.............................. 20.2 Boosting.......................................... 20.2.1 AdaBoost................................. 20.3 Bagging........................................... 20.4 Random Forests.................................... 20.5 Topics in Ensemble Learning....................... 20.6 Solving Multiclass Classification ................ 20.6.1 One-Against-All Strategy................. 20.6.2 One-Against-One Strategy................. 20.6.3 Error-Correcting Output Codes (ECOCs). . . 20.7 Dempster-Shafer Theory of Evidence................ References............................................... Introduction to Fuzzy Sets and Logic..................... 21.1 Introduction...................................... 21.2 Definitions and Terminologies..................... 21.3 Membership Function............................... 21.4 Intersection, Union, and Negation................. 21.5 Fuzzy Relation and Aggregation.................... 21.6 Fuzzy Implication................................. 21.7 Reasoning and Fuzzy Reasoning..................... 21.7.1 Modus Ponens and Modus Tollens........... 21.7.2 Generalized Modus Ponens................. 21.7.3 Fuzzy Reasoning Methods.................. 21.8 Fuzzy Inference Systems........................... 21.8.1 Fuzzy Rules and Fuzzy Interference....... 21.8.2 Fuzzification and Defuzzification ....... 21.9 Fuzzy Models...................................... 21.9.1 Mamdani Model............................ 21.9.2 Takagi-Sugeno-Kang Model................. 21.10 Complex Fuzzy Logic............................... 21.11 Possibility Theory................................ 21.12 Case-Based Reasoning.............................. 21.13 Granular Computing and Ontology................... References............................................... Neurofuzzy Systems....................................... 22.1 Introduction...................................... 22.1.1 Interpretability......................... Contents XXI 22.2 Rule Extraction from Trained Neural Networks............... 679 22.2.1 Fuzzy Rules and Multilayer Perceptrons.......... 679 22.2.2 Fuzzy Rules and RBF Networks.................... 680 22.2.3 Rule Extraction from SVMs......................... 681 22.2.4 Rule Generation from Other Neural Networks .... 682 22.3 Extracting Rules from Numerical data....................... 683 22.3.1 Rule Generation Based on Fuzzy Partitioning..... 684 22.3.2 Other Methods..................................... 685 22.4 Synergy of Fuzzy Logic and Neural Networks................. 687 22.5 ANFIS Model................................................ 688 22.6 Fuzzy SVMs................................................. 693 22.7 Other Neurofuzzy Models.................................. 696 References...................................................... 700 23 Neural. Circuits and Parallel Implementation....................... 705 23.1 Introduction................................................ 705 23.2 Flardware/Software Codesign................................ 707 23.3 Topics in Digital Circuit Designs.......................... 708 23.4 Circuits for Neural-Network Models......................... 709 23.4.1 Circuits for MLPs.................................. 709 23.4.2 Circuits for RBF Networks.......................... 711 23.4.3 Circuits for Clustering............................ 712 23.4.4 Circuits for SVMs................ ............... 712 23.4.5 Circuits of Other Models........................... 713 23.5 Fuzzy Neural Circuits....................................... 715 23.6 Graphic Processing Unit implementation...................... 716 23.7 Implementation Using Systolic Algorithms.................... 717 23.8 Implementation Using Parallel Computers..................... 718 23.9 Implementation Using Cloud Computing........................ 720 References...................................................... 721 24 24 Pattern Recognition for Biometrics and Bioinformatics.............. 727 24.1 Biometrics................................................. 728 24.1.1 Physiological Biometrics and Recognition........... 728 24.1.2 Behavioral Biometrics and Recognition............. 731 24.2 Face Detection and Recognition............................. 732 24.2.1 Face Detection .................................. 733 24.2.2 Face Recognition................... 734 24.3 Bioinformatics........................................... 736 24.3.1 Microarray Technology .................... 739 24.3.2 Motif Discovery, Sequence Alignment, Protein Folding, and Coclustering ..................... . 741 References ........................................................ 743 xxii Contents 25 Data Mining........................................................ 747 25.1 Introduction.............................................. 747 25.2 Document Representations for Text Categorization........ 748 25.3 Neural Network Approach to Data Mining.................... 750 25.3.1 Classification-Based Data Mining................. 750 25.3.2 Clustering-Based Data Mining..................... 752 25.3.3 Bayesian Network-Based Data Mining......... 755 25.4 Personalized Search....................................... 756 25.5 XML Format................................................ 759 25.6 Web Usage Mining.......................................... 760 25.7 Association Mining........................................ 763 25.8 Ranking Search Results.................................... 761 25.8.1 Surfer Models.................................... 762 25.8.2 PageRank Algorithm............................... 763 25.8.3 Hypertext induced Topic Search (HITS)............ 766 25.9 Data Warehousing.......................................... 767 25.10 Content-Based Image Retrieval. ........................... 768 25.11 E-mail Anti-Spamming...................................... 771 References....................................................... 773 Appendix A: Mathematical Preliminaries................................ 779 Appendix B: Benchmarks and Resources............................. 799 About the Authors..................................................... 813 Index 815
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id	DE-604.BV042923272
illustrated	Illustrated
indexdate	2024-07-10T07:12:55Z
institution	BVB
isbn	9781447155706
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-028350642
oclc_num	871583632
open_access_boolean
owner	DE-739
owner_facet	DE-739
physical	XXVII, 824 S. Ill. : graph. Darst.
publishDate	2014
publishDateSearch	2014
publishDateSort	2014
publisher	Springer
record_format	marc
spelling	Du, Ke-Lin Verfasser aut Neural networks and statistical learning Ke-Lin Du ; M. N. S. Swamy London Springer 2014 XXVII, 824 S. Ill. : graph. Darst. txt rdacontent n rdamedia nc rdacarrier Ingenieurwissenschaften Engineering Data mining Optical pattern recognition Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Neuronales Netz (DE-588)4226127-2 s DE-604 Swamy, M. N. S. Verfasser aut Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028350642&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Du, Ke-Lin Swamy, M. N. S. Neural networks and statistical learning Ingenieurwissenschaften Engineering Data mining Optical pattern recognition Maschinelles Lernen (DE-588)4193754-5 gnd Neuronales Netz (DE-588)4226127-2 gnd
subject_GND	(DE-588)4193754-5 (DE-588)4226127-2
title	Neural networks and statistical learning
title_auth	Neural networks and statistical learning
title_exact_search	Neural networks and statistical learning
title_full	Neural networks and statistical learning Ke-Lin Du ; M. N. S. Swamy
title_fullStr	Neural networks and statistical learning Ke-Lin Du ; M. N. S. Swamy
title_full_unstemmed	Neural networks and statistical learning Ke-Lin Du ; M. N. S. Swamy
title_short	Neural networks and statistical learning
title_sort	neural networks and statistical learning
topic	Ingenieurwissenschaften Engineering Data mining Optical pattern recognition Maschinelles Lernen (DE-588)4193754-5 gnd Neuronales Netz (DE-588)4226127-2 gnd
topic_facet	Ingenieurwissenschaften Engineering Data mining Optical pattern recognition Maschinelles Lernen Neuronales Netz
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028350642&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT dukelin neuralnetworksandstatisticallearning AT swamymns neuralnetworksandstatisticallearning

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