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Pattern Recognition: An Algorithmic Approach (Undergraduate Topics in Computer Science)

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Our main aim in writing this book is to make the concepts of pattern recognition clear to undergraduate and postgraduate students of the subject. We will not deal with pre-processing of data. Rather, assuming that patterns are represented using some appropriate pre-processing techniques in the form of vectors of numbers, we will describe algorithms that exploit numeric data in order to make important decisions like classification. Plenty of worked out examples have been included in the book along with a number of exercises at the end of each chapter.

The book would also be of use to researchers in the area. Readers interested in furthering their knowledge of the subject can benefit from the ‘‘Further Reading’’ section and the bibliography given at the end of each chapter.

Pattern recognition has applications in almost every field of human endeavour including geology, geography, astronomy and psychology. More specifically, it is useful in bioinformatics, psychological analysis, biometrics and a host of other applications. We believe that this book will be useful to all researchers who need to apply pattern recognition techniques to solve their problems.

We thank the following students for their critical comments on parts of the book: Vikas Garg, Abhay Yadav, Sathish Reddy, Deepak Kumar, Naga Malleswara Rao, Saikrishna, Sharath Chandra, and Kalyan.

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.

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