Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 18
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 FIGURE 2.11 The set ... d = 3 We now construct a set of N distinct K - dimensional hyperplanes , each ...
... < d ) in Ed . We desire to know the number Lz ( N , d ) of linear dichot ... dimensional hyperplane contains all of them . The set Z FIGURE 2.11 The set ... d = 3 We now construct a set of N distinct K - dimensional hyperplanes , each ...
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 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
2 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies 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 classifier pattern hyperplane pattern space pattern vector 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 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 |