Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 16
Sivu 11
... apply to a large class of discriminant functions and are therefore of funda- mental importance . The concept of a layered machine is introduced in Chapter 6. Most of the pattern classifiers containing threshold elements that have been ...
... apply to a large class of discriminant functions and are therefore of funda- mental importance . The concept of a layered machine is introduced in Chapter 6. Most of the pattern classifiers containing threshold elements that have been ...
Sivu 118
... apply something like the generalized error- correction procedure to a structure containing more than one subsidiary discriminant function per bank . The conditions ( if any ) under which this procedure terminates in a solution , when a ...
... apply something like the generalized error- correction procedure to a structure containing more than one subsidiary discriminant function per bank . The conditions ( if any ) under which this procedure terminates in a solution , when a ...
Sivu 122
... apply the closest - mode method is a means of training a PWL machine such that the modes are identified and the appropriate discriminant functions are set up . This training process should be an iterative one , operating on a sequence ...
... apply the closest - mode method is a means of training a PWL machine such that the modes are identified and the appropriate discriminant functions are set up . This training process should be an iterative one , operating on a sequence ...
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 |