Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu 82
... proved if we prove that S✩ terminates . = - W1 , W2 , k , 5.3 Proof 1 The following proof results from conflicting bounds on the growth rate of the length of the weight vector during the fixed - increment error- correction process ...
... proved if we prove that S✩ terminates . = - W1 , W2 , k , 5.3 Proof 1 The following proof results from conflicting bounds on the growth rate of the length of the weight vector during the fixed - increment error- correction process ...
Sivu 92
... proving the theorem , we shall show that the sequence S converges to a point P. = k For any fixed W in W let lim | W - W 1 ( W ) ; 1 ( W ) exists since Eq . ( 5.38 ) holds for all k . We conclude that Ŵ must converge to a hypersphere S ...
... proving the theorem , we shall show that the sequence S converges to a point P. = k For any fixed W in W let lim | W - W 1 ( W ) ; 1 ( W ) exists since Eq . ( 5.38 ) holds for all k . We conclude that Ŵ must converge to a hypersphere S ...
Sivu 113
... proved that the fixed - increment error - correction training method implied a bound on the length of the weight vectors , thus explaining some cases in which the committee machine cannot be successfully trained . REFERENCES 1 Farley ...
... proved that the fixed - increment error - correction training method implied a bound on the length of the weight vectors , thus explaining some cases in which the committee machine cannot be successfully trained . REFERENCES 1 Farley ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
LAYERED MACHINES | 95 |
Tekijänoikeudet | |
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assume belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X gi(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 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 shown in Fig solution weight vector Stanford subsets X1 subsidiary discriminant functions Suppose terns 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 |