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 210
... data set D of prototypes and a particular point x Є D , write a program to create a list of line segments comprising the Voronoi cell of x . ( c ) Use your program to form the Voronoi tesselation of the x and x2 features from the data ...
... data set D of prototypes and a particular point x Є D , write a program to create a list of line segments comprising the Voronoi cell of x . ( c ) Use your program to form the Voronoi tesselation of the x and x2 features from the data ...
Page 455
... data set , sampled independently , as is the training set . In some cases , this approach has some unexpected drawbacks when applied to comparing classifiers . In a discrete problem , for example , when the training set and test set are ...
... data set , sampled independently , as is the training set . In some cases , this approach has some unexpected drawbacks when applied to comparing classifiers . In a discrete problem , for example , when the training set and test set are ...
Page 483
... SET m - FOLD CROSS- VALIDATION In simple validation we randomly split the set of labeled training samples D into two ... data set D is split into two parts . The first ( e.g. , 90 % of the patterns ) is used as a standard training set ...
... SET m - FOLD CROSS- VALIDATION In simple validation we randomly split the set of labeled training samples D into two ... data set D is split into two parts . The first ( e.g. , 90 % of the patterns ) is used as a standard training set ...
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