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 224
... solution region Y2 solution region Y2 a separating plane Yı " separating " plane Yı FIGURE 5.8 . Four training samples ( black for w1 , red for @ 2 ) and the solution region in feature space . The figure on the left shows the raw data ...
... solution region Y2 solution region Y2 a separating plane Yı " separating " plane Yı FIGURE 5.8 . Four training samples ( black for w1 , red for @ 2 ) and the solution region in feature space . The figure on the left shows the raw data ...
Page 238
... solution region — that is , any vector satisfying â'y ; > b for all i — then at each step , a ( k ) gets closer to â ... solution region , the limiting a ( k ) is confined to the intersection of the hyperspheres centered about all of the ...
... solution region — that is , any vector satisfying â'y ; > b for all i — then at each step , a ( k ) gets closer to â ... solution region , the limiting a ( k ) is confined to the intersection of the hyperspheres centered about all of the ...
Page 257
... solution . If the answer is positive , there is no separating vector , but we have proof that the samples are nonseparable . τ * In the terminology of linear programming , any solution satisfying the constraints is called a feasible ...
... solution . If the answer is positive , there is no separating vector , but we have proof that the samples are nonseparable . τ * In the terminology of linear programming , any solution satisfying the constraints is called a feasible ...
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