Pattern Recognition: Statistical, Structural and Neural Approaches

ISBN-13: 9780471529743 / Angielski / Miękka / 1991 / 384 str.

Robert J. Schalkoff;Schalkoff

Opis

The heart of pattern recognition concepts, methods and applications are explored in this textbook, using statistical, syntactic and neural approaches. The book clearly demonstrates the similarities and differences among the three approaches and each chapter provides the reader with examples and pertinent literature for a more in-depth study of specific topics.

Spis treści

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.