Containing numerous exercises, this book deals with the scientific discipline that enables similar perception in machines through pattern recognition (PR). The book offers an exposition of principal topics in PR using an algorithmic approach.

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.

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

Autorentext
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.

Klappentext

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.

Inhalt

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.