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 294
... training data . In on- line training , each pattern is presented once and only once ; there is no use of memory for storing the patterns . * We describe the overall amount of pattern presentations by epoch - where one epoch corresponds ...
... training data . In on- line training , each pattern is presented once and only once ; there is no use of memory for storing the patterns . * We describe the overall amount of pattern presentations by epoch - where one epoch corresponds ...
Page 310
... Training with Noise When the training set is small , one can generate virtual or surrogate training patterns and use them as if they were normal training patterns sampled from the source distri- butions . In the absence of problem ...
... Training with Noise When the training set is small , one can generate virtual or surrogate training patterns and use them as if they were normal training patterns sampled from the source distri- butions . In the absence of problem ...
Page 478
... patterns in the training set D will be used . Conversely , if the problem is extremely difficult , then C1 will explain only a small amount of the data , and nearly all the patterns will be informative with respect to C1 ; thus n2 will ...
... patterns in the training set D will be used . Conversely , if the problem is extremely difficult , then C1 will explain only a small amount of the data , and nearly all the patterns will be informative with respect to C1 ; thus n2 will ...
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