Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 29
Sivu 32
... dimensional patterns , let L ( N , d ) be the number of linear dichotomies . L ( N , d ) is equal to twice the number of ways in which N points can be partitioned by a ( d 1 ) -dimensional hyperplane . ( For each distinct ... dimensions, 1.
... dimensional patterns , let L ( N , d ) be the number of linear dichotomies . L ( N , d ) is equal to twice the number of ways in which N points can be partitioned by a ( d 1 ) -dimensional hyperplane . ( For each distinct ... dimensions, 1.
Sivu 36
... d = 3 We now construct a set of N distinct K - dimensional hyperplanes , each containing Z and one of the points in X. Figure 2.11 illustrates this construction . Let H be a ( d- K ) -dimensional hyperplane intersecting each of the N K ...
... d = 3 We now construct a set of N distinct K - dimensional hyperplanes , each containing Z and one of the points in X. Figure 2.11 illustrates this construction . Let H be a ( d- K ) -dimensional hyperplane intersecting each of the N K ...
Sivu 89
... D dimensions each . Each D - dimensional vector Y in y will generate R 1 distinct RD- dimensional vectors in Z according to the following rules : - 1. Y will belong to one of the training subsets ; suppose it belongs to Yi . 2. We ...
... D dimensions each . Each D - dimensional vector Y in y will generate R 1 distinct RD- dimensional vectors in Z according to the following rules : - 1. Y will belong to one of the training subsets ; suppose it belongs to Yi . 2. We ...
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 |