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
... independent ( but could be conditionally independent ) . Furthermore , we may know several vari- ables that might influence another : The coolant temperature is affected by the engine temperature , the speed of the radiator fan ( which ...
... independent ( but could be conditionally independent ) . Furthermore , we may know several vari- ables that might influence another : The coolant temperature is affected by the engine temperature , the speed of the radiator fan ( which ...
Page 104
... independent of 0. Because we want to show that P ( DIO ) can be factored , our attention is directed toward computing P ( DIO ) in terms of P ( Ds , 0 ) . We do this by summing the joint probability P ( D , s | 0 ) over all values of s ...
... independent of 0. Because we want to show that P ( DIO ) can be factored , our attention is directed toward computing P ( DIO ) in terms of P ( Ds , 0 ) . We do this by summing the joint probability P ( D , s | 0 ) over all values of s ...
Page 620
... Independent Random Variables ( 85 ) It frequently happens that we know the densities for two independent random vari- ables x and y , and we need to know the density of their sum z = x + y . It is easy to obtain the mean and the ...
... Independent Random Variables ( 85 ) It frequently happens that we know the densities for two independent random vari- ables x and y , and we need to know the density of their sum z = x + y . It is easy to obtain the mean and 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