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Pattern Recognition

An Algorithmic Approach

  • M. Narasimha Murty
  • V. Susheela Devi

Part of the Undergraduate Topics in Computer Science book series (UTICS, volume 0)

Table of contents

  1. Front Matter
    Pages I-XI
  2. M. Narasimha Murty, V. Susheela Devi
    Pages 1-6
  3. M. Narasimha Murty, V. Susheela Devi
    Pages 7-47
  4. M. Narasimha Murty, V. Susheela Devi
    Pages 48-85
  5. M. Narasimha Murty, V. Susheela Devi
    Pages 86-102
  6. M. Narasimha Murty, V. Susheela Devi
    Pages 103-122
  7. M. Narasimha Murty, V. Susheela Devi
    Pages 123-146
  8. M. Narasimha Murty, V. Susheela Devi
    Pages 147-187
  9. M. Narasimha Murty, V. Susheela Devi
    Pages 188-206
  10. M. Narasimha Murty, V. Susheela Devi
    Pages 207-244
  11. M. Narasimha Murty, V. Susheela Devi
    Pages 245-246
  12. M. Narasimha Murty, V. Susheela Devi
    Pages 247-254
  13. Back Matter
    Pages 255-263

About this book

Introduction

Observing the environment, and recognising patterns for the purpose of decision-making, is fundamental to human nature. The scientific discipline of pattern recognition (PR) is devoted to how machines use computing to discern patterns in the real world.

This must-read textbook provides an exposition of principal topics in PR using an algorithmic approach. Presenting a thorough introduction to the concepts of PR and a systematic account of the major topics, the text also reviews the vast progress made in the field in recent years. The algorithmic approach makes the material more accessible to computer science and engineering students.

Topics and features:

  • Makes thorough use of examples and illustrations throughout the text, and includes end-of-chapter exercises and suggestions for further reading
  • Describes a range of classification methods, including nearest-neighbour classifiers, Bayes classifiers, and decision trees
  • Includes chapter-by-chapter learning objectives and summaries, as well as extensive referencing
  • Presents standard tools for machine learning and data mining, covering neural networks and support vector machines that use discriminant functions
  • Explains important aspects of PR in detail, such as clustering
  • Discusses hidden Markov models for speech and speaker recognition tasks, clarifying core concepts through simple examples

This concise and practical text/reference will perfectly meet the needs of senior undergraduate and postgraduate students of computer science and related disciplines. Additionally, the book will be useful to all researchers who need to apply PR techniques to solve their problems.

Dr. M. Narasimha Murty is a Professor in the Department of Computer Science and Automation at the Indian Institute of Science, Bangalore. Dr. V. Susheela Devi is a Senior Scientific Officer at the same institution.

Keywords

Bayes Classifier Combinational Classifiers Decision Trees Hidden Markov Models Hierarchical and Partitioning Schemes for Clustering Nearest Neighbour Classifiers Support Vector Machine

Authors and affiliations

  • M. Narasimha Murty
    • 1
  • V. Susheela Devi
    • 1
  1. 1.Dept. of Computer Science and AutomationIndian Institute of ScienceBangaloreIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-85729-495-1
  • Copyright Information Universities Press (India) Pvt. Ltd. 2011
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-0-85729-494-4
  • Online ISBN 978-0-85729-495-1
  • Series Print ISSN 1863-7310
  • Buy this book on publisher's site
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