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
Results 1-3 of 58
Page 285
... units . Cleary , such a network is an extension of the two - layer networks we studied in Chapter 5. The function of ... hidden unit computes the weighted sum of its inputs to form its scalar net activation which we denote simply as net .
... units . Cleary , such a network is an extension of the two - layer networks we studied in Chapter 5. The function of ... hidden unit computes the weighted sum of its inputs to form its scalar net activation which we denote simply as net .
Page 302
... hidden weights that are most instructive . In particular , such weights at a single hidden unit describe the input pattern that leads to maximum activation of that hidden unit , analogous to a " matched filter " ( Section 6.10.3 ) ...
... hidden weights that are most instructive . In particular , such weights at a single hidden unit describe the input pattern that leads to maximum activation of that hidden unit , analogous to a " matched filter " ( Section 6.10.3 ) ...
Page 367
... hidden units must be set . A popular alternative topology is obtained by eliminating interconnections among input units , as well as among output units . ( Such a network is faster to train but will be somewhat less ef- fective at ...
... hidden units must be set . A popular alternative topology is obtained by eliminating interconnections among input units , as well as among output units . ( Such a network is faster to train but will be somewhat less ef- fective at ...
Contents
MAXIMUMLIKELIHOOD AND BAYESIAN | 84 |
NONPARAMETRIC TECHNIQUES | 161 |
LINEAR DISCRIMINANT FUNCTIONS | 215 |
Copyright | |
10 other sections not shown
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