Neural networks and statistical learning:
Gespeichert in:
| Hauptverfasser: | , |
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| Format: | Buch |
| Sprache: | English |
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London
Springer
2014
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| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXVII, 824 S. Ill. : graph. Darst. |
| ISBN: | 9781447155706 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV042923272 | ||
| 003 | DE-604 | ||
| 005 | 20151015 | ||
| 007 | t | ||
| 008 | 151013s2014 ad|| |||| 00||| eng d | ||
| 020 | |a 9781447155706 |9 978-1-4471-5570-6 | ||
| 035 | |a (OCoLC)871583632 | ||
| 035 | |a (DE-599)HBZHT018122488 | ||
| 040 | |a DE-604 |b ger |e rakwb | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-739 | ||
| 084 | |a ST 300 |0 (DE-625)143650: |2 rvk | ||
| 100 | 1 | |a Du, Ke-Lin |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Neural networks and statistical learning |c Ke-Lin Du ; M. N. S. Swamy |
| 264 | 1 | |a London |b Springer |c 2014 | |
| 300 | |a XXVII, 824 S. |b Ill. : graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Ingenieurwissenschaften | |
| 650 | 4 | |a Engineering | |
| 650 | 4 | |a Data mining | |
| 650 | 4 | |a Optical pattern recognition | |
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Neuronales Netz |0 (DE-588)4226127-2 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 1 | |a Neuronales Netz |0 (DE-588)4226127-2 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Swamy, M. N. S. |e Verfasser |4 aut | |
| 856 | 4 | 2 | |m Digitalisierung UB Passau - ADAM Catalogue Enrichment |q application/pdf |u 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 |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-028350642 | ||
Datensatz im Suchindex
| _version_ | 1804175226327531520 |
|---|---|
| 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.........................
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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..............................................
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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.......................................
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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
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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
|
| any_adam_object | 1 |
| author | Du, Ke-Lin Swamy, M. N. S. |
| author_facet | Du, Ke-Lin Swamy, M. N. S. |
| author_role | aut aut |
| author_sort | Du, Ke-Lin |
| author_variant | k l d kld m n s s mns mnss |
| building | Verbundindex |
| bvnumber | BV042923272 |
| classification_rvk | ST 300 |
| ctrlnum | (OCoLC)871583632 (DE-599)HBZHT018122488 |
| discipline | Informatik |
| format | Book |
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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 |