Pattern Recognition and Classification

This volume, both comprehensive and accessible, introduces all the key concepts in pattern recognition, and includes many examples and exercises that make it an ideal guide to an important methodology widely deployed in today's ubiquitous automated systems.

The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner.

Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer vision. Fundamental concepts of supervised and unsupervised classification are presented in an informal, rather than axiomatic, treatment so that the reader can quickly acquire the necessary background for applying the concepts to real problems. More advanced topics, such as semi-supervised classification, combining clustering algorithms and relevance feedback are addressed in the laterchapters.

This book is suitable for undergraduates and graduates studying pattern recognition and machine learning.

A comprehensive yet accessible introduction to the core concepts behind pattern recognition Presents the funadmental concepts of supervised and unsupervised classification in an informal treatment, allowing the reader to quickly apply these concepts Contains exercises at the end of each chapter, with solutions available to instructors online Request lecturer material: sn.pub/lecturer-material

Autorentext
Geoff Dougherty is a Professor of Applied Physics and Medical Imaging at California State University, Channel Islands. He is the Author of Springer's Medical Image Processing, Techniques and Applications

Inhalt
Introduction.- Classification.- Nonmetric Methods.- Statistical Pattern Recognition.- Supervised Learning.- Nonparametric Learning.- Feature Extraction and Selection.- Unsupervised Learning.- Estimating and Comparing Classifiers.- Projects