Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 34
Sivu 37
... pattern space satisfying the equations g ( X ) = 0 are families of surfaces . The separations that these surfaces ( called surfaces ) effect on a set X of N points are called dichotomies . If there is no surface in the pattern space ...
... pattern space satisfying the equations g ( X ) = 0 are families of surfaces . The separations that these surfaces ( called surfaces ) effect on a set X of N points are called dichotomies . If there is no surface in the pattern space ...
Sivu 66
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. We recall that a TLU implements a hyperplane decision surface which divides the pattern space into two half - spaces . One of these half- spaces is R1 ; the other is ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. We recall that a TLU implements a hyperplane decision surface which divides the pattern space into two half - spaces . One of these half- spaces is R1 ; the other is ...
Sivu 105
... Pattern space 3 x2 1,4,5,8 TLU 3 Origin TLU 2 - * 3,7 TLU 1 ( b ) Image space 2 FIGURE 6.6 Pattern - space to image - space transformation numbers 1 , 2 , and 3 , we have an easy means of determining the trans- formation from the pattern ...
... Pattern space 3 x2 1,4,5,8 TLU 3 Origin TLU 2 - * 3,7 TLU 1 ( b ) Image space 2 FIGURE 6.6 Pattern - space to image - space transformation numbers 1 , 2 , and 3 , we have an easy means of determining the trans- formation from the pattern ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix second layer shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns training patterns training sequence training set training subsets transformation two-layer machine values W₁ weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |