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 163
... number n of samples and let V approach zero , the region will eventually become so small that it will enclose no samples , and our estimate p ( x ) ~ 0 will be useless . Or if by chance one or more of the training samples coincide at x ...
... number n of samples and let V approach zero , the region will eventually become so small that it will enclose no samples , and our estimate p ( x ) ~ 0 will be useless . Or if by chance one or more of the training samples coincide at x ...
Page 177
... samples , also drawn from the target distributions , a technique we shall explore in Chapter 9 . 4.4.2 Estimation of ... number of samples were captured , such as k = √n . In either case , as n goes to infinity an infinite number of ...
... samples , also drawn from the target distributions , a technique we shall explore in Chapter 9 . 4.4.2 Estimation of ... number of samples were captured , such as k = √n . In either case , as n goes to infinity an infinite number of ...
Page 492
... number of samples there is an optimal number of cells . This is di- rectly related to the fact that with a finite number of samples the performance will worsen if too many features are used . In this case it is clear why there exists an ...
... number of samples there is an optimal number of cells . This is di- rectly related to the fact that with a finite number of samples the performance will worsen if too many features are used . In this case it is clear why there exists an ...
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