Pattern recognition: statistical, structural, and neural approaches

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J. Wiley, 1992 - Psychology - 364 pages
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Explores the heart of pattern recognition concepts, methods and applications using statistical, syntactic and neural approaches. Divided into four sections, it clearly demonstrates the similarities and differences among the three approaches. The second part deals with the statistical pattern recognition approach, starting with a simple example and finishing with unsupervised learning through clustering. Section three discusses the syntactic approach and explores such topics as the capabilities of string grammars and parsing; higher dimensional representations and graphical approaches. Part four presents an excellent overview of the emerging neural approach including an examination of pattern associations and feedforward nets. Along with examples, each chapter provides the reader with pertinent literature for a more in-depth study of specific topics.

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Contents

STATISTICAL PATTERN RECOGNITION
33
The Gaussian Case and Class Dependence
39
Extensions
47
Classifier Performance Risk and Errors
49
SUPERVISED LEARNING TRAINING
58
Supervised Learning Using Nonparametric Approaches
66
knn Nonparametric Estimation
74
Bibliographical Remarks
83
Bibliographical Remarks
200
Physical Neural Networks
211
INTRODUCTION TO NEURAL PATTERN
221
Bibliographical Remarks
233
FEEDFORWARD NETWORKS
236
Extension of the DR for Units in the Hidden Layers The
246
Bibliographical and Historical Remarks
258
CONTENT ADDRESSABLE MEMORY
264

LINEAR DISCRIMINANT FUNCTIONS
89
UNSUPERVISED LEARNING
109
Bibliographical Remarks
121
SYNTACTIC PATTERN RECOGNITION
127
SYNTACTIC RECOGNITION VIA PARSING
155
Augmented Transition Networks ATNs in Parsing
163
Stochastic Grammars and Applications
170
GRAPHICAL APPROACHES
176
LEARNING VIA GRAMMATICAL
194
Linear Algebra Review
289
Probability and Random VariablesVectors
298
Discrete Mathematics Review
314
Examples and Error Function
323
Geometry for Classification and StateSpace
337
REFERENCES
344
PERMISSION SOURCE NOTES
360
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