Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu ix
... discriminant functions , 15 2.2 Linear discriminant functions , 16 2.3 Minimum - distance classifiers , 16 2.4 The decision surfaces of linear machines , 18 2.5 Linear classifications of patterns , 20 2.6 The threshold logic unit ( TLU ) ...
... discriminant functions , 15 2.2 Linear discriminant functions , 16 2.3 Minimum - distance classifiers , 16 2.4 The decision surfaces of linear machines , 18 2.5 Linear classifications of patterns , 20 2.6 The threshold logic unit ( TLU ) ...
Sivu 16
... discriminant functions belonging to a particular family can then be accomplished by adjusting the values of the parame- ters . We shall often call these ... DISCRIMINANT FUNCTIONS Linear discriminant functions, Minimum-distance classifiers,
... discriminant functions belonging to a particular family can then be accomplished by adjusting the values of the parame- ters . We shall often call these ... DISCRIMINANT FUNCTIONS Linear discriminant functions, Minimum-distance classifiers,
Sivu 24
... discriminant function , is given by an expression of the form g . ( X ) = Wilx1 + Wi2DX2 + + WidXa + wid + 1 ( 2 · 19 ) X H w il w il +1 w Pattern il 24 SOME IMPORTANT DISCRIMINANT FUNCTIONS Piecewise linear discriminant functions,
... discriminant function , is given by an expression of the form g . ( X ) = Wilx1 + Wi2DX2 + + WidXa + wid + 1 ( 2 · 19 ) X H w il w il +1 w Pattern il 24 SOME IMPORTANT DISCRIMINANT FUNCTIONS Piecewise linear discriminant functions,
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |