An edition of Pattern Recognition and Classification (2012)

Pattern Recognition and Classification

An Introduction

by Geoff Dougherty

Read

My Reading Lists:

Use this Work

Create a new list

Download Options

Check nearby libraries

WorldCat

Buy this book

Last edited by MARC Bot

September 12, 2024 | History

Edit

An edition of Pattern Recognition and Classification (2012)

Pattern Recognition and Classification

An Introduction

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.

Publish Date

2013

Publisher

Springer New York, Imprint: Springer

Language

English

Pages

196

Check nearby libraries

WorldCat

Buy this book

Previews available in: English

Edition	Availability
1 Pattern Recognition and Classification: An Introduction Apr 30, 2017, Springer paperback 1493953354 9781493953356	zzzz Locate
2 Pattern Recognition and Classification: An Introduction 2013, Springer New York, Imprint: Springer electronic resource : in English 1461453232 9781461453239	aaaa Read Listen Locate
3 Pattern Recognition and Classification: An Introduction Oct 29, 2012, Springer paperback 1461453240 9781461453246	zzzz Locate

Add another edition?

Book Details

Preface

Acknowledgments

Chapter 1 Introduction

1.1 Overview

1.2 Classification

1.3 Organization of the Book

Bibliography

Exercises

Chapter 2 Classification

2.1 The Classification Process

2.2 Features

2.3 Training and Learning

2.4 Supervised Learning and Algorithm Selection

2.5 Approaches to Classification

2.6 Examples

2.6.1 Classification by Shape

2.6.2 Classification by Size

2.6.3 More Examples

2.6.4 Classification of Letters

Bibliography

Exercises

Chapter 3 Non-Metric Methods

3.1 Introduction

3.2 Decision Tree Classifier

3.2.1 Information, Entropy and Impurity

3.2.2 Information Gain

3.2.3 Decision Tree Issues

3.2.4 Strengths and Weaknesses

3.3 Rule-Based Classifier

3.4 Other Methods

Bibliography

Exercises

Chapter 4 Statistical Pattern Recognition

4.1 Measured Data and Measurement Errors

4.2 Probability Theory

4.2.1 Simple Probability Theory

4.2.2 Conditional Probability and Bayes’ Rule

4.2.3 Naïve Bayes classifier

4.3 Continuous Random Variables

4.3.1 The Multivariate Gaussian

4.3.2 The Covariance Matrix

4.3.3 The Mahalanobis Distance

Bibliography

Exercises

Chapter 5 Supervised Learning

5.1 Parametric and Non-Parametric Learning

5.2 Parametric Learning

5.2.1 Bayesian Decision Theory

5.2.2 Discriminant Functions and Decision Boundaries

5.2.3 MAP (Maximum A Posteriori) Estimator

Bibliography

Exercises

Chapter 6 Non-Parametric Learning

6.1 Histogram Estimator and Parzen Windows

6.2 k-Nearest Neighbor (k-NN) Classification

6.3 Artificial Neural Networks (ANNs)

6.4 Kernel Machines

Bibliography

Exercises

Chapter 7 Feature Extraction and Selection

7.1 Reducing Dimensionality

7.1.1 Pre-Processing

7.2 Feature Selection

7.2.1 Inter/Intra-Class Distance

7.2.2 Subset Selection

7.3 Feature Extraction

7.3.1 Principal Component Analysis (PCA)

7.3.2 Linear Discriminant Analysis (LDA)

Bibliography

Exercises

Chapter 8 Unsupervised Learning

8.1 Clustering

8.2 k-Means Clustering

8.2.1 Fuzzy c-Means Clustering

8.3 (Agglomerative) Hierarchical Clustering

Bibliography

Exercises

Chapter 9 Estimating and Comparing Classifiers

9.1 Comparing Classifiers and the No Free Lunch Theorem

9.1.2 Bias and Variance

9.2 Cross-Validation and Resampling Methods

9.2.1 The Holdout Method

9.2.2 k-Fold Cross-Validation

9.2.3 Bootstrap

9.3 Measuring Classifier Performance

9.4 Comparing Classifiers

9.4.1 ROC curves

9.4.2 McNemar’s Test

9.4.3 Other Statistical Tests

9.4.4 The Classification Toolbox

9.5 Combining classifiers

Bibliography

Chapter 10 Projects

10.1 Retinal Tortuosity as an Indicator of Disease

10.2 Segmentation by Texture

10.3 Biometric Systems

10.3.1 Fingerprint Recognition

10.3.2 Face Recognition

Bibliography

Index.

Edition Notes

Published in: New York, NY

Classifications

Dewey Decimal Class: 006.4
Library of Congress: Q337.5, TK7882.P3, QA75.5-76.95

The Physical Object

Format: [electronic resource] :
Pagination: XI, 196 p. 158 illus., 104 illus. in color.
Number of pages: 196

Edition Identifiers

Open Library: OL27080005M
Internet Archive: patternrecogniti00doug
ISBN 13: 9781461453239

Work Identifiers

Work ID: OL19893738W

Source records

Internet Archive item record
Internet Archive item record
Better World Books record
Harvard University record

Community Reviews (0)

No community reviews have been submitted for this work.

Lists

History

Created July 6, 2019
4 revisions

Download catalog record: RDF / JSON / OPDS | Wikipedia citation

September 12, 2024	Edited by MARC Bot	import existing book
January 27, 2022	Edited by ImportBot	import existing book
July 6, 2019	Edited by MARC Bot	import existing book
July 6, 2019	Created by MARC Bot	Imported from Internet Archive item record

Pattern Recognition and Classification

An Introduction

by Geoff Dougherty