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 57
... NODE There are many cases where we know or can safely assume which variables are or are not independent , even ... node ( or unit ) represents one of the system variables , and here it takes on discrete values . We will label nodes with ...
... NODE There are many cases where we know or can safely assume which variables are or are not independent , even ... node ( or unit ) represents one of the system variables , and here it takes on discrete values . We will label nodes with ...
Page 395
... node is displayed at the top , connected by successive ( directional ) links or branches to other nodes . These are similarly con- nected until we reach terminal or leaf nodes , which have no further links ( Fig . 8.1 ) . Sections 8.3 ...
... node is displayed at the top , connected by successive ( directional ) links or branches to other nodes . These are similarly con- nected until we reach terminal or leaf nodes , which have no further links ( Fig . 8.1 ) . Sections 8.3 ...
Page 398
... Node Impurity PURITY ENTROPY IMPURITY Much of the work in designing trees focuses on deciding which property test or query should be performed at each node . * With nonnumeric data , there is no geometrical interpretation of how the ...
... Node Impurity PURITY ENTROPY IMPURITY Much of the work in designing trees focuses on deciding which property test or query should be performed at each node . * With nonnumeric data , there is no geometrical interpretation of how the ...
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