Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 14
Sivu 32
... dichotomies of N points of 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 ...
... dichotomies of N points of 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 ...
Sivu 33
... dichotomies of X ' . We wish to find out by how much this number of linear dichotomies is increased if the set X ' is enlarged to - " X ,. 2 X 2 X , 3 4 2 3 4 le 5 6x3 X3 1 ls ( a ) Points in general position ( b ) Three points ...
... dichotomies of X ' . We wish to find out by how much this number of linear dichotomies is increased if the set X ' is enlarged to - " X ,. 2 X 2 X , 3 4 2 3 4 le 5 6x3 X3 1 ls ( a ) Points in general position ( b ) Three points ...
Sivu 37
... dichotomies . If there is no surface in the pattern space containing M + 1 or more members of X , then we say that the members of X are in general position . For any function family , we desire to know the number Ž ( N , d ) of dichotomies ...
... dichotomies . If there is no surface in the pattern space containing M + 1 or more members of X , then we say that the members of X are in general position . For any function family , we desire to know the number Ž ( N , d ) of dichotomies ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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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,Lįszló Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |
Kirjaluettelon tiedot
| Otsikko | Learning Machines: Foundations of Trainable Pattern-classifying Systems McGraw-Hill series in systems science |
| Kirjoittaja | Nils J. Nilsson |
| Painos | kuvitettu |
| Kustantaja | McGraw-Hill, 1965 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |