Pattern recognition and neural networks

Başlık:

Yazar:

Ripley, Brian D., 1952-

ISBN:

9780521717700

Ek Yazar:

Ripley, Brian D., 1952-

Yayım Bilgisi:

Cambridge ; New York : Cambridge University Press, 2007.

Fiziksel Tanım:

[xii], 403 p. : ill. ; 25 cm.

Contents:

Introduction and examples -- Statistical decision theory -- Linear discriminant analysis -- Flexible discriminants -- Feed-forward neural networks -- Non-parametric methods -- Tree-structured classifiers -- Belief networks -- Unsupervised methods -- Finding good pattern features -- Statistical sidelines.

Konu Terimleri:

Sinir ağları (Bilgisayar bilimi).

Örüntü tanıma sistemleri.

Neural networks (Computer science)

Pattern recognition systems.

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Library	Materyal Türü	Barkod	Yer Numarası	Durum
Searching... Pamukkale Merkez Kütüphanesi	Kitap	0049177	QA76.87.R56 2007	Searching... Unknown

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Özet

This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theory and the enormously diverse applications (which can be found in remote sensing, astrophysics, engineering and medicine, for example). So that readers can develop their skills and understanding, many of the real data sets used in the book are available from the author's website: www.stats.ox.ac.uk/~ripley/PRbook/. For the same reason, many examples are included to illustrate real problems in pattern recognition. Unifying principles are highlighted, and the author gives an overview of the state of the subject, making the book valuable to experienced researchers in statistics, machine learning/artificial intelligence and engineering. The clear writing style means that the book is also a superb introduction for non-specialists.

Preface	p. ix
Notation	p. xii
1 Introduction and Examples	p. 1
1.1 How do neural methods differ?	p. 4
1.2 The pattern recognition task	p. 5
1.3 Overview of the remaining chapters	p. 9
1.4 Examples	p. 10
1.5 Literature	p. 15
2 Statistical Decision Theory	p. 17
2.1 Bayes rules for known distributions	p. 18
2.2 Parametric models	p. 26
2.3 Logistic discrimination	p. 43
2.4 Predictive classification	p. 45
2.5 Alternative estimation procedures	p. 55
2.6 How complex a model do we need?	p. 59
2.7 Performance assessment	p. 66
2.8 Computational learning approaches	p. 77
3 Linear Discriminant Analysis	p. 91
3.1 Classical linear discrimination	p. 92
3.2 Linear discriminants via regression	p. 101
3.3 Robustness	p. 105
3.4 Shrinkage methods	p. 106
3.5 Logistic discrimination	p. 109
3.6 Linear separation and perceptrons	p. 116
4 Flexible Discriminants	p. 121
4.1 Fitting smooth parametric functions	p. 122
4.2 Radial basis functions	p. 131
4.3 Regularization	p. 136
5 Feed-forward Neural Networks	p. 143
5.1 Biological motivation	p. 145
5.2 Theory	p. 147
5.3 Learning algorithms	p. 148
5.4 Examples	p. 160
5.5 Bayesian perspectives	p. 163
5.6 Network complexity	p. 168
5.7 Approximation results	p. 173
6 Non-parametric Methods	p. 181
6.1 Non-parametric estimation of class densities	p. 181
6.2 Nearest neighbour methods	p. 191
6.3 Learning vector quantization	p. 201
6.4 Mixture representations	p. 207
7 Tree-structured Classifiers	p. 213
7.1 Splitting rules	p. 216
7.2 Pruning rules	p. 221
7.3 Missing values	p. 231
7.4 Earlier approaches	p. 235
7.5 Refinements	p. 237
7.6 Relationships to neural networks	p. 240
7.7 Bayesian trees	p. 241
8 Belief Networks	p. 243
8.1 Graphical models and networks	p. 246
8.2 Causal networks	p. 262
8.3 Learning the network structure	p. 275
8.4 Boltzmann machines	p. 279
8.5 Hierarchical mixtures of experts	p. 283
9 Unsupervised Methods	p. 287
9.1 Projection methods	p. 288
9.2 Multidimensional scaling	p. 305
9.3 Clustering algorithms	p. 311
9.4 Self-organizing maps	p. 322
10 Finding Good Pattern Features	p. 327
10.1 Bounds for the Bayes error	p. 328
10.2 Normal class distributions	p. 329
10.3 Branch-and-bound techniques	p. 330
10.4 Feature extraction	p. 331
A Statistical Sidelines	p. 333
A.1 Maximum likelihood and MAP estimation	p. 333
A.2 The EM algorithm	p. 334
A.3 Markov chain Monte Carlo	p. 337
A.4 Axioms for conditional independence	p. 339
A.5 Optimization	p. 342
Glossary	p. 347
References	p. 355
Author Index	p. 391
Subject Index	p. 399

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