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 196
... FIGURE 4.26 . During training of an RCE network , each pattern has a parameter- equivalent to a radius in the d - dimensional space - that is adjusted to be as large as possible without enclosing any points from a different category ...
... FIGURE 4.26 . During training of an RCE network , each pattern has a parameter- equivalent to a radius in the d - dimensional space - that is adjusted to be as large as possible without enclosing any points from a different category ...
Page 481
... FIGURE 9.8 . Active learning can be used to create classifiers that are more accurate than ones using i.i.d. sampling . The figure at the top shows a two - dimensional problem with two equal circular Gaussian priors ; the Bayes decision ...
... FIGURE 9.8 . Active learning can be used to create classifiers that are more accurate than ones using i.i.d. sampling . The figure at the top shows a two - dimensional problem with two equal circular Gaussian priors ; the Bayes decision ...
Page 554
... FIGURE 10.13 . Two Gaussians were used to generate two - dimensional samples , shown in pink and black . The nearest - neighbor clustering algorithm gives two clusters that well approximate the generating Gaussians ( left ) . If ...
... FIGURE 10.13 . Two Gaussians were used to generate two - dimensional samples , shown in pink and black . The nearest - neighbor clustering algorithm gives two clusters that well approximate the generating Gaussians ( left ) . If ...
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