Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 84
Sivu 69
... training patterns are presented to the trainable TLU one at a time for trial . The trial consists of comparing the actual response of the TLU with the desired response dictated by the category of the pattern . The patterns may be tried ...
... training patterns are presented to the trainable TLU one at a time for trial . The trial consists of comparing the actual response of the TLU with the desired response dictated by the category of the pattern . The patterns may be tried ...
Sivu 120
... patterns in each of the training subsets . Next we select some metric with which to measure distance in the pattern space ; for example , we might select ordinary Euclidean distance . Now , to classify an arbitrary pattern X , we pool the ...
... patterns in each of the training subsets . Next we select some metric with which to measure distance in the pattern space ; for example , we might select ordinary Euclidean distance . Now , to classify an arbitrary pattern X , we pool the ...
Sivu 121
... pattern - classification tasks . To classify any pat- tern X , the distance between X and each of the patterns in the training subsets must be computed . If these computations are to be performed rapidly , each of the training patterns ...
... pattern - classification tasks . To classify any pat- tern X , the distance between X and each of the patterns in the training subsets must be computed . If these computations are to be performed rapidly , each of the training patterns ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose 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 |