Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 30
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 ... number of linear dichotomies of N points dimensions,
... 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 ... number of linear dichotomies of N points dimensions,
Sivu 41
... number of linear dichotomies and the extension of these results to surfaces are also due to Cover . " Based on experimental and theoretical results on the number of linear dichotomies , both Koford12 and Brown13 suggested that the ...
... number of linear dichotomies and the extension of these results to surfaces are also due to Cover . " Based on experimental and theoretical results on the number of linear dichotomies , both Koford12 and Brown13 suggested that the ...
Sivu 67
... linear dichotomy of the N patterns , and , conversely , a dichotomy of the patterns in y is a linear dichotomy only if there is a region in weight space corresponding to it . * For any given linear dichotomy , the corre- * If we count the ...
... linear dichotomy of the N patterns , and , conversely , a dichotomy of the patterns in y is a linear dichotomy only if there is a region in weight space corresponding to it . * For any given linear dichotomy , the corre- * If we count the ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components Computer 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 Stanford step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |