Pattern Classification, Part 1This 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. |
From inside the book
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Page 100
... Bayes Methods Differ ? For reasonable prior distributions that do not preclude the true solution , maximum- likelihood and Bayes solutions are equivalent in the asymptotic limit of infinite train- ing data . However since practical ...
... Bayes Methods Differ ? For reasonable prior distributions that do not preclude the true solution , maximum- likelihood and Bayes solutions are equivalent in the asymptotic limit of infinite train- ing data . However since practical ...
Page 303
... BAYES THEORY AND PROBABILITY While multilayer neural networks may appear to be somewhat ad hoc , we now show that when trained via backpropagation on a sum - squared ... Bayes Theory and Probability, Bayes Discriminants and Neural Networks,
... BAYES THEORY AND PROBABILITY While multilayer neural networks may appear to be somewhat ad hoc , we now show that when trained via backpropagation on a sum - squared ... Bayes Theory and Probability, Bayes Discriminants and Neural Networks,
Page 638
... Bayes comparison nearest - neighbor relation , 178 Bayes error dependence on number of features , 110 Bayes estimation maximum - likelihood comparison , 100 Bayes rule , 615 , 616 , 620 model , 486 vector , 617 Bayesian learning , see ...
... Bayes comparison nearest - neighbor relation , 178 Bayes error dependence on number of features , 110 Bayes estimation maximum - likelihood comparison , 100 Bayes rule , 615 , 616 , 620 model , 486 vector , 617 Bayesian learning , see ...
Contents
MAXIMUMLIKELIHOOD AND BAYESIAN | 84 |
NONPARAMETRIC TECHNIQUES | 161 |
LINEAR DISCRIMINANT FUNCTIONS | 215 |
Copyright | |
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Other editions - View all
Computer Manual in MATLAB to accompany Pattern Classification David G. Stork,Elad Yom-Tov No preview available - 2004 |
Computer Manual in MATLAB to accompany Pattern Classification David G. Stork,Elad Yom-Tov No preview available - 2004 |
Common terms and phrases
analysis approach assume backpropagation Bayes Bayesian bias binary Boltzmann calculate Chapter cluster centers component classifiers Consider convergence corresponding covariance matrix criterion function d-dimensional data set decision boundary denote derivation discriminant function distance distribution entropy error rate feature space FIGURE Gaussian given gradient descent Hidden Markov Models hidden units independent input iteration jackknife estimate labeled large number learning algorithm maximum-likelihood estimate mean methods minimize minimum minimum description length mixture density nearest-neighbor neural networks node nonlinear normal number of clusters number of samples obtain optimal output units p(xw parameters pattern recognition Perceptron points prior probabilities probability density problem procedure random variables randomly Section sequence shown shows simple solution split statistical statistically independent string Suppose target tion training data training error training patterns training set tree two-category unsupervised learning variance w₁ weight vector x₁ zero