Pattern Recognition and Neural Networks

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Cambridge University Press, 2007 - Computers - 403 pages
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Pattern recognition has long been studied in relation to many different (and mainly unrelated) applications, such as remote sensing, computer vision, space research, and medical imaging. In this book Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks. Unifying principles are brought to the fore, and the author gives an overview of the state of the subject. Many examples are included to illustrate real problems in pattern recognition and how to overcome them.This is a self-contained account, ideal both as an introduction for non-specialists readers, and also as a handbook for the more expert reader.
 

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Contents

Introduction and Examples
1
11 How do neural methods differ?
4
12 The pattern recognition task
5
13 Overview of the remaining chapters
9
14 Examples
10
15 Literature
15
Statistical Decision Theory
17
21 Bayes rules for known distributions
18
63 Learning vector quantization
201
64 Mixture representations
207
Treestructured Classifiers
213
71 Splitting rules
216
72 Pruning rules
221
73 Missing values
231
74 Earlier approaches
235
75 Refinements
237

22 Parametric models
26
23 Logistic discrimination
43
24 Predictive classification
45
25 Alternative estimation procedures
55
26 How complex a model do we need?
59
27 Performance assessment
66
28 Computational learning approaches
77
Linear Discriminant Analysis
91
31 Classical linear discrimination
92
32 Linear discriminants via regression
101
33 Robustness
105
34 Shrinkage methods
106
35 Logistic discrimination
109
36 Linear separation and perceptrons
116
Flexible Discriminants
121
41 Fitting smooth parametric functions
122
42 Radial basis functions
131
43 Regularization
136
Feedforward Neural Networks
143
51 Biological motivation
145
52 Theory
147
53 Learning algorithms
148
54 Examples
160
55 Bayesian perspectives
163
56 Network complexity
168
57 Approximation results
173
Nonparametric Methods
181
62 Nearest neighbour methods
191
76 Relationships to neural networks
240
77 Bayesian trees
241
Belief Networks
243
81 Graphical models and networks
246
82 Causal networks
262
83 Learning the network structure
275
84 Boltzmann machines
279
85 Hierarchical mixtures of experts
283
Unsupervised Methods
287
91 Projection methods
288
92 Multidimensional scaling
305
93 Clustering algorithms
311
94 Selforganizing maps
322
Finding Good Pattern Features
327
101 Bounds for the Bayes error
328
102 Normal class distributions
329
103 Branchandbound techniques
330
104 Feature extraction
331
Statistical Sidelines
333
A2 The EM algorithm
334
A3 Markov chain Monte Carlo
337
A4 Axioms for conditional independence
339
A5 Optimization
342
Glossary
347
References
355
Author Index
391
Subject Index
399
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About the author (2007)

Brian Ripley is the Professor of Applied Statistics at the University of Oxford and a member of the Department of Statistics as well as a Professorial Fellow of St. Peter's College.

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