Neural networks for pattern recognition:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Oxford [u.a.]
Oxford Univ. Press
2010
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| Ausgabe: | Reprint. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XVII, 482 S. zahlr. graph. Darst. |
| ISBN: | 9780198538646 |
Internformat
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Datensatz im Suchindex
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| adam_text | CONTENTS 1 Statistical Pattern Recognition 1.1 An example - character recognition 1.2 Classification and regression 1.3 Pre-processing and feature extraction 1.4 The curse of dimensionality 1.5 Polynomial curve fitting 1.6 Model complexity 1.7 Multivariate non-linear functions 1.8 Bayes’ theorem 1.9 Decision boundaries 1.10 Minimizing risk Exercises 1 1 5 6 7 9 14 15 17 23 27 28 2 Probability Density Estimation Parametric methods 2.1 2.2 Maximum likelihood 2.3 Bayesian inference 2.4 Sequential parameter estimation 2.5 Non-parametric methods 2.6 Mixture models Exercises 33 34 39 42 46 49 59 73 Single-Layer Networks 3 3.1 Linear discriminant functions 3.2 Linear separability 3.3 Generalized linear discriminants 3.4 Least-squares techniques 3.5 The perceptron 3.6 Fisher’s linear discriminant Exercises 77 77 85 88 89 98 105 112 The Multi-layer Perceptron 4 4.1 Feed-forward network mappings 4.2 Threshold units 4.3 Sigmoidal units 4.4 Weight-space symmetries 4.5 Higher-order networks 4.6 Projection pursuit regression 4.7 Kolmogorov’s theorem 116 116 121 126 133 133 135 137
xvi Contents 4.8 Error back-propagation 4.9 The Jacobian matrix 4.10 The Hessian matrix Exercises 140 148 150 161 5 Radial Basis Functions 5.1 Exact interpolation 5.2 Radial basis function networks 5.3 Network training 5.4 Regularization theory 5.5 Noisy interpolation theory 5.6 Relation to kernel regression 5.7 Radial basis function networks for classification 5.8 Comparison with the multi-layer perceptron 5.9 Basis function optimization 5.10 Supervised training Exercises 164 164 167 170 171 176 177 179 182 183 190 191 6 Error Functions 6.1 Sum-of-squares error 6.2 Minkowski error 6.3 Input-dependent variance 6.4 Modelling conditional distributions 6.5 Estimating posterior probabilities 6.6 Sum-of-squares for classification 6.7 Cross-entropy for two classes 6.8 Multiple independent attributes 6.9 Cross-entropy for multiple classes 6.10 Entropy 6.11 General conditions for outputs to be probabilities Exercises 194 195 208 211 212 222 225 230 236 237 240 245 248 Parameter Optimization Algorithms 7 Error surfaces 7.1 7.2 Local quadratic approximation 7.3 Linear output units 7.4 Optimization in practice 7.5 Gradient descent 7.6 Line search 7.7 Conjugate gradients 7.8 Scaled conjugate gradients 7.9 Newton’s method 7.10 Quasi-Newton methods 7.11 The Levenberg-Marquardt algorithm Exercises 253 254 257 259 260 263 272 274 282 285 287 290 292
Contents 8 Pre-processing and Feature Extraction Pre-processing and post-processing 8.1 Input normalization and encoding 8.2 Missing data 8.3 Time series prediction 8.4 Feature selection 8.5 Principal component analysis 8.6 8.7 Invariances and prior knowledge Exercises xvii 295 296 298 301 302 304 310 . 319 329 Learning and Generalization 9 9.1 Bias and variance Regularization 9.2 Training with noise 9.3 9.4 Soft weight sharing Growing and pruning algorithms 9.5 Committees of networks 9.6 Mixtures of experts 9.7 Model order selection 9.8 Vapnik-Chervonenkis dimension 9.9 Exercises 332 333 338 346 349 353 364 369 371 377 380 10 Bayesian Techniques 10.1 Bayesian learning of network weights 10.2 Distribution of network outputs 10.3 Application to classification problems 10.4 The evidence framework for a and ß 10.5 Integration over hyperparameters 10.6 Bayesian model comparison 10.7 Committees of networks 10.8 Practical implementation of Bayesian techniques 10.9 Monte Carlo methods 10.10 Minimum description length Exercises 385 387 398 403 406 415 418 422 424 425 429 433 A Symmetric Matrices 440 В Gaussian Integrals 444 C Lagrange Multipliers 448 D Calculus of Variations 451 E Principal Components 454 References 457 Index 477
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| indexdate | 2024-07-09T22:55:27Z |
| institution | BVB |
| isbn | 9780198538646 |
| language | English |
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| spelling | Bishop, Christopher M. 1959- Verfasser (DE-588)120454165 aut Neural networks for pattern recognition Christopher M. Bishop Reprint. Oxford [u.a.] Oxford Univ. Press 2010 XVII, 482 S. zahlr. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf Mustererkennung (DE-588)4040936-3 s Neuronales Netz (DE-588)4226127-2 s DE-604 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021205849&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bishop, Christopher M. 1959- Neural networks for pattern recognition Neuronales Netz (DE-588)4226127-2 gnd Mustererkennung (DE-588)4040936-3 gnd |
| subject_GND | (DE-588)4226127-2 (DE-588)4040936-3 |
| title | Neural networks for pattern recognition |
| title_auth | Neural networks for pattern recognition |
| title_exact_search | Neural networks for pattern recognition |
| title_full | Neural networks for pattern recognition Christopher M. Bishop |
| title_fullStr | Neural networks for pattern recognition Christopher M. Bishop |
| title_full_unstemmed | Neural networks for pattern recognition Christopher M. Bishop |
| title_short | Neural networks for pattern recognition |
| title_sort | neural networks for pattern recognition |
| topic | Neuronales Netz (DE-588)4226127-2 gnd Mustererkennung (DE-588)4040936-3 gnd |
| topic_facet | Neuronales Netz Mustererkennung |
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