Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 33
Sivu 32
... number of dichotomies of N patterns that its members could effect . We shall show that if the positions of the N pattern points satisfy some quite mild conditions , the number of dichotomies that can be implemented by a function will ...
... number of dichotomies of N patterns that its members could effect . We shall show that if the positions of the N pattern points satisfy some quite mild conditions , the number of dichotomies that can be implemented by a function will ...
Sivu 120
... patterns in the training subsets . Many of these nonparametric rules actually lead to the same discriminant ... number of patterns in each of the training subsets . Next we select some metric with which to measure distance in the pattern ...
... patterns in the training subsets . Many of these nonparametric rules actually lead to the same discriminant ... number of patterns in each of the training subsets . Next we select some metric with which to measure distance in the pattern ...
Sivu 121
... number of patterns in the training subsets . The value of k / N , however , should decrease toward zero with increasing N. The high storage requirements of the Fix and Hodges method render it impractical in most pattern - classification ...
... number of patterns in the training subsets . The value of k / N , however , should decrease toward zero with increasing N. The high storage requirements of the Fix and Hodges method render it impractical in most pattern - classification ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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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 |