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 114
... approach is to assume a weighted combination of the equal and individual covariances , a technique known as shrinkage ( also called regularized dis- criminant analysis ) , because the individual covariances " shrink " toward a common ...
... approach is to assume a weighted combination of the equal and individual covariances , a technique known as shrinkage ( also called regularized dis- criminant analysis ) , because the individual covariances " shrink " toward a common ...
Page 167
... approach zero , but at a rate slower than 1 / n . We shall now see why these are the basic conditions for ... approach p ( x ) as n approaches infinity . 4.3.2 Convergence of the Variance Equation 23 shows that there is no need for an ...
... approach zero , but at a rate slower than 1 / n . We shall now see why these are the basic conditions for ... approach p ( x ) as n approaches infinity . 4.3.2 Convergence of the Variance Equation 23 shows that there is no need for an ...
Page 491
... approach zero , and by favoring hard problems we can make P approach 0.5 . We would like to choose a prior distribution corresponding to the class of problems we typically encounter , but there is no obvious way to do that . A bold approach ...
... approach zero , and by favoring hard problems we can make P approach 0.5 . We would like to choose a prior distribution corresponding to the class of problems we typically encounter , but there is no obvious way to do that . A bold approach ...
Contents
MAXIMUMLIKELIHOOD AND BAYESIAN | 84 |
NONPARAMETRIC TECHNIQUES | 161 |
LINEAR DISCRIMINANT FUNCTIONS | 215 |
Copyright | |
10 other sections not shown
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