Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 30
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... 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 ...
Sivu 104
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. ponents of +1 and -1 . Thus , each point in the pattern space is trans- formed into one of the vertices of a Pi - dimensional hypercube . This hypercube we shall call ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. ponents of +1 and -1 . Thus , each point in the pattern space is trans- formed into one of the vertices of a Pi - dimensional hypercube . This hypercube we shall call ...
Sisältö
Preface vii | 11 |
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 important 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 |