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 401
... split that sends 70 w patterns to the right along with O 2 patterns , and sends 20 w1 and 10 2 to the left . This is an attractive split , but the misclassification impurity is still 0.1 . On the other hand , the Gini impurity for this ...
... split that sends 70 w patterns to the right along with O 2 patterns , and sends 20 w1 and 10 2 to the left . This is an attractive split , but the misclassification impurity is still 0.1 . On the other hand , the Gini impurity for this ...
Page 403
... split does not reduce the impurity significantly , splitting is stopped ( Problem 15 ) . - A variation in this ... split is " meaningful " —that is , whether it differs significantly from a random split . Suppose n patterns survive at ...
... split does not reduce the impurity significantly , splitting is stopped ( Problem 15 ) . - A variation in this ... split is " meaningful " —that is , whether it differs significantly from a random split . Suppose n patterns survive at ...
Page 410
... split is defined similarly , being the one that uses another feature and best approximates the primary split in this way . Of course , during classification of a deficient test pat- tern , we use the first surrogate split that does not ...
... split is defined similarly , being the one that uses another feature and best approximates the primary split in this way . Of course , during classification of a deficient test pat- tern , we use the first surrogate split that does not ...
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