Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 23
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... d for K = = 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 ...
... d for K = = 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 ...
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 : to Yi . 1. Y will belong to one of the training subsets ; suppose it belongs -- 2. We denote ...
... 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 : to Yi . 1. Y will belong to one of the training subsets ; suppose it belongs -- 2. We denote ...
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
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |