Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 18
Sivu 9
... parameters , some of whose values are unknown . If the values of these parameters were known , ade- quate discriminant functions based on them could be directly specified . In the parametric training methods the training set is used for ...
... parameters , some of whose values are unknown . If the values of these parameters were known , ade- quate discriminant functions based on them could be directly specified . In the parametric training methods the training set is used for ...
Sivu 43
... parameters ( for example , cluster points ) . The values of these parameters might be unknown a priori . If the parameters were known , we assume that discriminant functions based on them could have been readily specified . Parametric ...
... parameters ( for example , cluster points ) . The values of these parameters might be unknown a priori . If the parameters were known , we assume that discriminant functions based on them could have been readily specified . Parametric ...
Sivu 44
... parameters , whose values might also be unknown , are the a priori probabilities for each class p ( i ) , i = 1 ... parameters of the function p ( Xi ) . The parametric training method for the design of discriminant functions then ...
... parameters , whose values might also be unknown , are the a priori probabilities for each class p ( i ) , i = 1 ... parameters of the function p ( Xi ) . The parametric training method for the design of discriminant functions then ...
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 |