Pattern Recognition Statistical, Structural and Neural Approaches

ISBN-10: 0471529745
ISBN-13: 9780471529743
Edition: 1992
List price: $241.95 Buy it from $3.00
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Description: 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  More...

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Book details

List price: $241.95
Copyright year: 1992
Publisher: John Wiley & Sons, Incorporated
Publication date: 6/21/1991
Binding: Paperback
Pages: 384
Size: 8.00" wide x 9.75" long x 1.00" tall
Weight: 1.848
Language: English

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.

Statistical Pattern Recognition (StatPR)
Supervised Learning (Training) Using Parametric and Nonparametric Approaches
Linear Discriminant Functions and the Discrete and Binary Feature Cases
Unsupervised Learning and Clustering
Syntactic Pattern Recognition (SyntPR)
Overview
Syntactic Recognition via Parsing and Other Grammars
Graphical Approaches to SyntPR
Learning via Grammatical Inference
Neural Pattern Recognition (NeurPR)
Introduction to Neural Networks
Introduction to Neural Pattern Associators and Matrix Approaches
Feedforward Networks and Training by Backpropagation
Content Addressable Memory Approaches and Unsupervised Learning in NeurPR
Appendices
References
Permission Source Notes
Index

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