About this book
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.
- 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
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