Pattern recognition and classification : an introduction

The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classificati...

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Bibliographic Details
Main Author: Dougherty, Geoff, , 1950- (Author)
Format: Book
Language:English
Published: New York : Springer , c2013
Subjects:

MARC

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008 130530s2013 nyu eng
020 |a 1461453224 (hardback : alk. paper) 
020 |a 1461453232 (ebook) 
020 |a 9781461453222 (hardback : alk. paper) 
020 |a 9781461453239 (ebook) 
050 0 0 |a TK7882.P3   |b D68 2013 
090 0 0 |a TK7882.P3   |b D68 2013 
100 1 |a Dougherty, Geoff, ,   |d 1950- ,   |e author 
245 1 0 |a Pattern recognition and classification :   |b an introduction   |c Geoff Dougherty 
260 |a New York :   |b Springer ,   |c c2013 
300 |a xi, 196 p. :   |b ill. (some col.) ;   |c 24 cm. 
504 |a Includes bibliographical references and index 
505 0 |a 1. Introduction -- 2. Classification -- 3. Non-metric methods -- 4. Statistical pattern recognition -- 5. Supervised learning -- 6. Non-parametric learning -- 7. Feature extraction and selection -- 8. Unsupervised learning -- 9. Estimating and comparing classifiers -- 10. Projects 
520 |a The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer vision. Fundamental concepts of supervised and unsupervised classification are presented in an informal, rather than axiomatic, treatment so that the reader can quickly acquire the necessary background for applying the concepts to real problems. More advanced topics, such as estimating classifier performance and combining classifiers, and details of particular project applications are addressed in the later chapters. This book is suitable for undergraduates and graduates studying pattern recognition and machine learning. 
650 0 |a Algorithms 
650 0 |a Biology   |x Data processing 
650 0 |a Computer Appl. in Life Sciences 
650 0 |a Computer science 
650 0 |a Nonlinear Dynamics 
650 0 |a Optical pattern recognition 
650 0 |a Pattern perception 
650 0 |a Signal, Image and Speech Processing 
999 |a 1000159041   |b Book   |c OPEN SHELF (30 DAYS)   |e Gong Badak Campus