Pattern Recognition

1. Front Matter

   Pages I-XI

   PDF

2. Introduction

   • M. Narasimha Murty, V. Susheela Devi

   Pages 1-6

3. Representation

   • M. Narasimha Murty, V. Susheela Devi

   Pages 7-47

4. Nearest Neighbour Based Classifiers

   • M. Narasimha Murty, V. Susheela Devi

   Pages 48-85

5. Bayes Classifier

   • M. Narasimha Murty, V. Susheela Devi

   Pages 86-102

6. Hidden Markov Models

   • M. Narasimha Murty, V. Susheela Devi

   Pages 103-122

7. Decision Trees

   • M. Narasimha Murty, V. Susheela Devi

   Pages 123-146

8. Support Vector Machines

   • M. Narasimha Murty, V. Susheela Devi

   Pages 147-187

9. Combination of Classifiers

   • M. Narasimha Murty, V. Susheela Devi

   Pages 188-206

10. Clustering

    • M. Narasimha Murty, V. Susheela Devi

    Pages 207-244

11. Summary

    • M. Narasimha Murty, V. Susheela Devi

    Pages 245-246

12. An Application: Handwritten Digit Recognition

    • M. Narasimha Murty, V. Susheela Devi

    Pages 247-254

13. Back Matter

    Pages 255-263

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Observing the environment and recognising patterns for the purpose of decision making is fundamental to human nature. This book deals with the scientific discipline that enables similar perception in machines through pattern recognition (PR), which has application in diverse technology areas. This book is an exposition of principal topics in PR using an algorithmic approach. It provides a thorough introduction to the concepts of PR and a systematic account of the major topics in PR besides reviewing the vast progress made in the field in recent times. It includes basic techniques of PR, neural networks, support vector machines and decision trees. While theoretical aspects have been given due coverage, the emphasis is more on the practical. The book is replete with examples and illustrations and includes chapter-end exercises. It is designed to meet the needs of senior undergraduate and postgraduate students of computer science and allied disciplines.

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

From the reviews:

“This interesting book provides a concise and simple exposition of principal topics in pattern recognition using an algorithmic approach, and is intended mainly for undergraduate and postgraduate students. … An application to handwritten digit recognition is described at the end of the book. Many examples and exercises are proposed to make the treatment clear. A ‘further reading’ section and a bibliography are presented at the end of each chapter.” (Patrizio Frosini, Zentralblatt MATH, Vol. 1238, 2012)

• Dept. of Computer Science and Automation, Indian Institute of Science, Bangalore, India

  M. Narasimha Murty, V. Susheela Devi

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 in the Department of Computer Science and Automation at the Indian Institute of Science, Bangalore.

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