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 32
... entropy . We discuss entropy in greater detail in Appendix Section A.7 , but for now we merely state that the entropy of a distribution is given by H ( p ( x ) ) = - -fi p ( x ) In p ( x ) dx , ( 37 ) and measured in nats ; if a log ...
... entropy . We discuss entropy in greater detail in Appendix Section A.7 , but for now we merely state that the entropy of a distribution is given by H ( p ( x ) ) = - -fi p ( x ) In p ( x ) dx , ( 37 ) and measured in nats ; if a log ...
Page 631
... entropy distribution ( H = 0 bits ) —we are certain as to the symbol that will appear . For a continuous distribution , the entropy is DIRAC DELTA ∞ H = - - JP p ( x ) In p ( x ) dx , ( 118 ) and again H = E [ In 1 / p ] . It is worth ...
... entropy distribution ( H = 0 bits ) —we are certain as to the symbol that will appear . For a continuous distribution , the entropy is DIRAC DELTA ∞ H = - - JP p ( x ) In p ( x ) dx , ( 118 ) and again H = E [ In 1 / p ] . It is worth ...
Page 632
... entropy and differences in entropy are more fundamental than H taken by itself . Nevertheless , questions of the foundations of entropy measures for contin- uous variables are addressed in books listed in Bibliographical Remarks . A.7.2 ...
... entropy and differences in entropy are more fundamental than H taken by itself . Nevertheless , questions of the foundations of entropy measures for contin- uous variables are addressed in books listed in Bibliographical Remarks . A.7.2 ...
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