Introduction.- Representation.- Nearest Neighbour Based Classifiers.- Bayes Classifier.- Hidden Markov Models.- Decision Trees.- Support Vector Machines.- Combination of Classifiers.- Clustering.- Summary.- An Application: Handwritten Digit Recognition.

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 readingDescribes a range of classification methods, including nearest-neighbour classifiers, Bayes classifiers, and decision treesIncludes chapter-by-chapter learning objectives and summaries, as well as extensive referencingPresents standard tools for machine learning and data mining, covering neural networks and support vector machines that use discriminant functionsExplains important aspects of PR in detail, such as clusteringDiscusses hidden Markov models for speech and speaker recognition tasks, clarifying core concepts through simple examplesThis 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.

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)

Contains numerous exercises, as well as learning objectives and summaries for each chapter Explains the hidden Markov model for speech and speaker recognition tasks Discusses support vector machines, with suitable examples