Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu 98
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. But consider the " committee " of weight vectors W1 , W2 , and Wa in Fig . 6.3 . With respect to these weight vectors , we have the inequalities 1 1 W2 Y1 < 0 2 · W1 ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. But consider the " committee " of weight vectors W1 , W2 , and Wa in Fig . 6.3 . With respect to these weight vectors , we have the inequalities 1 1 W2 Y1 < 0 2 · W1 ...
Sivu 99
... committee machine with a fixed vote - taking TLU . x1 x2 Response X Pattern xd + 1 = +1 P committee TLUS ( first layer ) Vote - taking TLU ( second layer ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee machines ...
... committee machine with a fixed vote - taking TLU . x1 x2 Response X Pattern xd + 1 = +1 P committee TLUS ( first layer ) Vote - taking TLU ( second layer ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee machines ...
Sivu 100
... machine , one at a time , for trial . The procedure is similar to the error ... machine . Suppose that at the kth stage of the process a pattern Yk ... committee TLUS have negative responses . If the responses of at least . 11⁄2 ...
... machine , one at a time , for trial . The procedure is similar to the error ... machine . Suppose that at the kth stage of the process a pattern Yk ... committee TLUS have negative responses . If the responses of at least . 11⁄2 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted assume augmented pattern belonging to category binary called Chapter cluster committee machine components Cornell Aeronautical Laboratory correction increment covariance matrix d-dimensional decision regions decision surfaces denote density function discussed dot products equal error-correction procedure Euclidean distance example Fix and Hodges fixed-increment error-correction function family g₁(X gi(X given hypersphere image-space implemented initial weight vectors layered machine linear dichotomies linear discriminant functions linearly separable loss function Lx(i mean vector minimum-distance classifier number of linear number of patterns optimum classifier parameters partition pattern classifier pattern hyperplane pattern points pattern space pattern vector pattern-classifying machines patterns belonging Perceptron piecewise linear point sets positive probability distributions prototype pattern PWL machine quadratic form quadric discriminant function quadric function sample covariance matrix solution weight vector Stanford subsets X1 Suppose training patterns training sequence training set training subsets values W₁ wa+1 weight point weight space X₁ X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |