Pattern Recognition and Classification

An Introduction

  • Geoff Dougherty

Table of contents

  1. Front Matter
    Pages i-xi
  2. Geoff Dougherty
    Pages 1-7
  3. Geoff Dougherty
    Pages 9-26
  4. Geoff Dougherty
    Pages 27-41
  5. Geoff Dougherty
    Pages 43-74
  6. Geoff Dougherty
    Pages 75-98
  7. Geoff Dougherty
    Pages 99-121
  8. Geoff Dougherty
    Pages 123-141
  9. Geoff Dougherty
    Pages 143-155
  10. Geoff Dougherty
    Pages 157-176
  11. Geoff Dougherty
    Pages 177-188
  12. Back Matter
    Pages 189-196

About this book


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 estimating classifier performance and combining classifiers, and details of particular project applications are addressed in the later chapters.

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


Bayesian Decision Theory Clustering Techniques Feature Extraction Machine Learning Neural Networks Object Recognition Pattern Recognition

Authors and affiliations

  • Geoff Dougherty
    • 1
  1. 1., Applied Physics and Medical ImagingCalifornia State Univ., Channel IslandsCamarilloUSA

Bibliographic information

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