Pattern Classification 2E - DUDA, RICHARD OSWALD

Pattern Classification 2E


Yayınevi: John Wiley High Education

Yayın tarihi: 12/2000

ISBN: 9780471056690

Yazar : Peter E. Hart David G. Stork

Ciltli | İngilizce | 654 Sayfa | Büyük boy  | 

Tür: Bilgisayar-Akademik

  • 495,00 TL

Practitioners developing or investigating pattern recognition systems in such diverse application areas as speech recognition, optical character recognition, image processing, or signal analysis, often face the difficult task of having to decide among a bewildering array of available techniques. This unique text/professional reference provides the information you need to choose the most appropriate method for a given class of problems, presenting an in-depth, systematic account of the major topics in pattern recognition today. A new edition of a classic work that helped define the field for over a quarter century, this practical book updates and expands the original work, focusing on pattern classification and the immense progress it has experienced in recent years. Special features include:

Clear explanations of both classical and new methods, including neural networks, stochastic methods, genetic algorithms, and theory of learning
Over 350 high-quality, two-color illustrations highlighting various concepts
Numerous worked examples
Pseudocode for pattern recognition algorithms
Expanded problems, keyed specifically to the text
Complete exercises, linked to the text
Algorithms to explain specific pattern-recognition and learning techniques
Historical remarks and important references at the end of chapters
Appendices covering the necessary mathematical background

NOTE: Computer Manual in MATLAB to Accompany Pattern Classification, 2e users access toolbox via ftp://ftp.wiley.com/public/sci_tech_med/pattern_classification/ (Note: Visitors will require a password from the Manual to access.)

Table of Contents:
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

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