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 95
... obtained the a posteriori density for the mean , p ( uD ) , all that remains is to obtain the " class - conditional " density for p ( x | D ) . * From Eqs . 25 , 26 and 30 we have p ( x [ D ) = √ p ( x \ u ) p ( μ \ D ) dμ 1 1 μ 1 2πση ...
... obtained the a posteriori density for the mean , p ( uD ) , all that remains is to obtain the " class - conditional " density for p ( x | D ) . * From Eqs . 25 , 26 and 30 we have p ( x [ D ) = √ p ( x \ u ) p ( μ \ D ) dμ 1 1 μ 1 2πση ...
Page 116
... obtain ak = e ' ( xk — m ) . - ( 83 ) Geometrically , this result merely says that we obtain a least - squares solution by pro- jecting the vector x onto the line in the direction of e that passes through the sample mean . This brings ...
... obtain ak = e ' ( xk — m ) . - ( 83 ) Geometrically , this result merely says that we obtain a least - squares solution by pro- jecting the vector x onto the line in the direction of e that passes through the sample mean . This brings ...
Page 251
... obtain a zero error vector , the algorithm automatically terminates with a solution vector . Suppose now that e ( k ) ... obtain an e ( k ) with no positive components . This would be most unfortunate , because we would have Ya ( k ) < b ...
... obtain a zero error vector , the algorithm automatically terminates with a solution vector . Suppose now that e ( k ) ... obtain an e ( k ) with no positive components . This would be most unfortunate , because we would have Ya ( k ) < b ...
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