Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 82
Sivu 69
... pattern in the training set be tried several times . The patterns may be presented by cycling through the training set , over and over , or the patterns may be presented in some random order as long as the trial of each one recurs . When ...
... pattern in the training set be tried several times . The patterns may be presented by cycling through the training set , over and over , or the patterns may be presented in some random order as long as the trial of each one recurs . When ...
Sivu 120
... patterns in the training subsets . Many of these nonparametric rules actually lead to the same discriminant functions that would be obtained by parametric training and the assumptions that the pattern probability distributions are ...
... patterns in the training subsets . Many of these nonparametric rules actually lead to the same discriminant functions that would be obtained by parametric training and the assumptions that the pattern probability distributions are ...
Sivu 122
... training patterns to a given pattern X will often include a predominant number of patterns from the cluster sur- rounding the closest mode . Thus the " closest - mode " method just de- scribed will often make decisions identical to ...
... training patterns to a given pattern X will often include a predominant number of patterns from the cluster sur- rounding the closest mode . Thus the " closest - mode " method just de- scribed will often make decisions identical to ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |