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. |
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Page 54
... particular known ways . How can we classify such corrupted inputs to obtain a minimum error now ? There are two analytically solvable cases of particular interest : when some of the features are missing , and when they are corrupted by ...
... particular known ways . How can we classify such corrupted inputs to obtain a minimum error now ? There are two analytically solvable cases of particular interest : when some of the features are missing , and when they are corrupted by ...
Page 131
... particular sequence of T visible states VT , we should take each conceivable sequence of hidden states , calculate the probability they produce VT , and then add up these probabilities . The probability of a particular visible sequence ...
... particular sequence of T visible states VT , we should take each conceivable sequence of hidden states , calculate the probability they produce VT , and then add up these probabilities . The probability of a particular visible sequence ...
Page 634
... particular O ( x3 ) algorithm is simpler than a particular O ( x2 ) algorithm , and it is occasionally necessary for us to determine these constants to find which of several implemementations is the simplest . Nevertheless , for our ...
... particular O ( x3 ) algorithm is simpler than a particular O ( x2 ) algorithm , and it is occasionally necessary for us to determine these constants to find which of several implemementations is the simplest . Nevertheless , for our ...
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
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 defined 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