Pattern Classification
2nd Edition
Published on 21. November 2000
Book
Hardback
XXIV, 656 pages
978-0-471-05669-0 (ISBN)
Description
The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.
Reviews / Votes
"...provides information needed to choose the most appropriate of the many available technique for a given class of problems." (SciTech Book News, Vol. 25, No. 2, June 2001) "This book is the unique text/professional reference for any serious student or worker in the field of pattern recognition." (Mathematical Reviews, Issue 2001k) "...strongly recommended both as a professional reference and as a text for students..." (Technometrics, February 2002) "...gives a systematic overview about the major topics in pattern recognition, based whenever possible on fundamental principles." (Zentralblatt MATH, Vol. 968, 2001/18) "attractively presented and readable" (Journal of Classification, Vol.18, No.2 2001)More details
Edition
2. Auflage
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
College/higher education
Professional and scholarly
Edition type
New edition
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 261 mm
Width: 184 mm
Thickness: 30 mm
Weight
1196 gr
ISBN-13
978-0-471-05669-0 (9780471056690)
Schweitzer Classification
Other editions
New editions
Additional editions
Richard O. Duda | Peter E. Hart | David G. Stork
Pattern Classification
E-Book
11/2012
2nd Edition
Wiley
€168.99
Available for download
Previous edition
Richard O. Duda | Peter E. Hart
Pattern Classification and Scene Analysis
Book
02/1973
Wiley
€80.47
Article exhausted; check for reprint
Persons
RICHARD O. DUDA, PhD, is Professor in the Electrical Engineering Department at San Jose State University, San Jose, California.
PETER E. HART, PhD, is Chief Executive Officer and President of Ricoh Innovations, Inc. in Menlo Park, California.
DAVID G. STORK, PhD, is Chief Scientist, also at Ricoh Innovations, Inc.
Content
Bayesian Decision Theory.
Maximum-Likelihood and Bayesian Parameter Estimation.
Nonparametric Techniques.
Linear Discriminant Functions.
Multilayer Neural Networks.
Stochastic Methods.
Nonmetric Methods.
Algorithm-Independent Machine Learning.
Unsupervised Learning and Clustering.
Appendix.
Index.