Pattern Recognition
An Algorithmic Approach
1st Edition
Published on 25. May 2011
XI, 263 pages
978-0-85729-495-1 (ISBN)
Description
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.
Reviews / Votes
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)More details
Other editions
Additional editions
Book
07/2011
1st Edition
Springer
€37.40
Shipment within 15-20 days
Persons
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.
Content
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.
