Pattern Recognition
Statistical, Structural and Neural Approaches
Published on 7. June 1991
Book
Paperback/Softback
388 pages
978-0-471-52974-3 (ISBN)
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.
More details
Persons
Robert J. Schalkoff is the author of Pattern Recognition: Statistical, Structural and Neural Approaches, published by Wiley.
Content
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.