Pattern Recognition and Classification
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Pattern Recognition and Classification : An Introduction

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Description

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 later chapters.

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

  • Paperback | 196 pages
  • 155 x 235 x 11.18mm | 3,226g
  • New York, United States
  • English
  • Softcover reprint of the original 1st ed. 2013
  • 104 Illustrations, color; 54 Illustrations, black and white; XI, 196 p. 158 illus., 104 illus. in color.
  • 1493953354
  • 9781493953356
  • 1,286,571

Back cover copy

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.
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Table of contents

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

From the reviews:
"The book is a concise introduction to the concepts of pattern recognition and classification. ... this book is accessible to mathematicians, computer scientists or biomedical engineers. The material of the book is presented in a very simple and accessible way. The author gives many examples presenting the notations and problems which are considered, so it makes the learning easier. ... chapters end up with exercises, which help to consolidate the gained knowledge." (Krzystof Gdawiec, Zentralblatt MATH, Vol. 1263, 2013)
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Review quote

From the reviews:

"The book is a concise introduction to the concepts of pattern recognition and classification. ... this book is accessible to mathematicians, computer scientists or biomedical engineers. The material of the book is presented in a very simple and accessible way. The author gives many examples presenting the notations and problems which are considered, so it makes the learning easier. ... chapters end up with exercises, which help to consolidate the gained knowledge." (Krzystof Gdawiec, Zentralblatt MATH, Vol. 1263, 2013)
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About Geoff Dougherty

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
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Rating details

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