Pattern Classification, Part 1
Wiley, 2001 - Computers - 654 pages
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."--BOOK JACKET.
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MAXIMUMLIKELIHOOD AND BAYESIAN
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algorithm analysis applied approach assume Bayes Bayesian bias bound calculate called Chapter classifier clusters complexity component computational conditional Consider convergence corresponding criterion decision defined density depends derivation described determine discriminant distance distribution equal error estimate example expected FIGURE final function Gaussian give given hidden units independent input instance known labeled leads learning linear matching matrix maximum-likelihood mean measure methods minimize natural neural networks node normal Note obtain optimal output parameters particular pattern pattern recognition performance points positive possible presented prior probability problem procedure random reference region represent requires rule samples selected separable sequence Show shown simple single solution space split statistical string Suppose theory tion tree true units variables variance vector weights Write zero