Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 40
Sivu 89
... suppose that Y1 , Y2 , ... , YR are linearly separable with a set of solution weight vectors W1 , W2 , ... , WR ; then observe that Z is linearly contained with an RD- dimensional vector V ( W1 , W2 , . . . , WR ) . Conversely , suppose ...
... suppose that Y1 , Y2 , ... , YR are linearly separable with a set of solution weight vectors W1 , W2 , ... , WR ; then observe that Z is linearly contained with an RD- dimensional vector V ( W1 , W2 , . . . , WR ) . Conversely , suppose ...
Sivu 92
... Suppose it is not , but instead lies outside * at a distance A from one of the pattern hyperplanes bounding W. But the pattern corresponding to this hyperplane will eventually occur in Sy , say at the kth step . Suppose k to be ...
... Suppose it is not , but instead lies outside * at a distance A from one of the pattern hyperplanes bounding W. But the pattern corresponding to this hyperplane will eventually occur in Sy , say at the kth step . Suppose k to be ...
Sivu 123
... Suppose that patterns are presented to the PWL machine one at a time from a training sequence . Let the initial weight vectors be selected arbitrarily . * We shall describe the adjustments to be made at the kth step . Suppose that the ...
... Suppose that patterns are presented to the PWL machine one at a time from a training sequence . Let the initial weight vectors be selected arbitrarily . * We shall describe the adjustments to be made at the kth step . Suppose that the ...
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 |