Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 14
Sivu 66
... space into two half - spaces . One of these half- spaces is R1 ; the other is R2 . The hyperplane separating these half - spaces is determined by the TLU weights w1 , w2 , . . . , wa , Wa + 1 . Training a TLU to dichotomize ... Weight space,
... space into two half - spaces . One of these half- spaces is R1 ; the other is R2 . The hyperplane separating these half - spaces is determined by the TLU weights w1 , w2 , . . . , wa , Wa + 1 . Training a TLU to dichotomize ... Weight space,
Sivu 72
... weight space . The small arrows attached to these planes in this case indicate the side on which a TLU weight vector will give the desired response . The patterns will be presented cyclically in the following order : 1 , 2 , 3 , 4 , 1 ...
... weight space . The small arrows attached to these planes in this case indicate the side on which a TLU weight vector will give the desired response . The patterns will be presented cyclically in the following order : 1 , 2 , 3 , 4 , 1 ...
Sivu 85
... weight vectors satisfying inequality ( 5-6 ) . That is , W.Y > 0 for all W ... space representation , it is clear that the boundaries of W ' are ... weight vector in the reduced weight - vector sequence Sŵ . That is j | W - W1 | 2 = W. W ...
... weight vectors satisfying inequality ( 5-6 ) . That is , W.Y > 0 for all W ... space representation , it is clear that the boundaries of W ' are ... weight vector in the reduced weight - vector sequence Sŵ . That is j | W - W1 | 2 = W. W ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X 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 partition pattern classifier pattern hyperplane pattern space pattern vector patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ Wa+1 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 |