Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 32
... d dimensions We shall begin by calculating the number of dichotomies of N patterns achievable by a linear discriminant function ( i.e. , a TLU ) . Recall that each of these dichotomies is called a linear dichotomy . For N d - dimensional ...
... d dimensions We shall begin by calculating the number of dichotomies of N patterns achievable by a linear discriminant function ( i.e. , a TLU ) . Recall that each of these dichotomies is called a linear dichotomy . For N d - dimensional ...
Sivu 36
... < d ) in Ed . We desire to know the number Lz ( N , d ) of linear dichot ... dimensional hyperplane contains all of them . The set Z The set consists of ... d for K = = 3 We now construct a set of N distinct K - dimensional hyperplanes ...
... < d ) in Ed . We desire to know the number Lz ( N , d ) of linear dichot ... dimensional hyperplane contains all of them . The set Z The set consists of ... d for K = = 3 We now construct a set of N distinct K - dimensional hyperplanes ...
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 ...
... 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 ...
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 |