Pattern Classification

Richard O. Duda (Author), Peter E. Hart (Author), David G. Stork (Author)

9780471056690, Wiley

Hardback, published 21 November 2000

688 pages
25.6 x 18.5 x 3.8 cm, 1.225 kg

"...it provides a good introduction to the subject of Pattern Classification." (Journal of Classification, September 2007)

"...a fantastic book! The presentation...could not be better, and I recommend that future authors consider...this book as a role model." (Journal of Statistical Computation and Simulation, March 2006)

"...strongly recommended both as a professional reference and as a text for students..." (Technometrics, February 2002)

"...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)

"I do not believe anybody wishing to teach or do serious work on Pattern Recognition can ignore this book, as it is the sort of book one wishes to find the time to read from cover to cover!" (Pattern Analysis & Applications Journal, 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)

"...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)

Unter Musterklassifikation versteht man die Zuordnung eines physikalischen Objektes zu einer von mehreren vordefinierten Kategorien. Auf dieser Grundlage können Computer Muster erkennen. Das Interesse an diesem Forschungsgebiet hat in den letzten Jahren, besonders im Zuge der Weiterentwicklung neuronaler Netze, stark zugenommen. Die umfassend überarbeitete, erweiterte und jetzt zweifarbig gestaltete Neuauflage beschreibt alle wesentlichen Aspekte der Mustererkennung systematisch und verständlich. Mit Lösungsheft! (01/00)

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.

Subject Areas: Electronics & communications engineering [TJ]