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. |
From inside the book
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Page 314
... stochastic gradient descent by Eq . 37 ( red arrows ) reduces the variation in overall gradient directions and ... stochastic variations in weight updates during stochastic learning . By increasing stability , it can speed the learning ...
... stochastic gradient descent by Eq . 37 ( red arrows ) reduces the variation in overall gradient directions and ... stochastic variations in weight updates during stochastic learning . By increasing stability , it can speed the learning ...
Page 316
... stochastic protocols . Batch learning is typically slower than stochastic learning . To see this , imag- ine a training set of 50 patterns that consists of 10 copies each of five patterns ( x ' , x2 , ... , x5 ) . In batch learning ...
... stochastic protocols . Batch learning is typically slower than stochastic learning . To see this , imag- ine a training set of 50 patterns that consists of 10 copies each of five patterns ( x ' , x2 , ... , x5 ) . In batch learning ...
Page 381
... stochastic techniques - ones that at some level rely on randomness to find model parameters . Simulated annealing , based on physical annealing of metals , con- sists in randomly perturbing the system , and gradually decreasing the ...
... stochastic techniques - ones that at some level rely on randomness to find model parameters . Simulated annealing , based on physical annealing of metals , con- sists in randomly perturbing the system , and gradually decreasing the ...
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