Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 15
... ranges , is called a family of functions . A particular function belonging to this family can be selected 15 SOME IMPORTANT DISCRIMINANT FUNCTIONS: THEIR PROPERTIES AND THEIR IMPLEMENTATIONS Families of discriminant functions,
... ranges , is called a family of functions . A particular function belonging to this family can be selected 15 SOME IMPORTANT DISCRIMINANT FUNCTIONS: THEIR PROPERTIES AND THEIR IMPLEMENTATIONS Families of discriminant functions,
Sivu 16
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. A particular function belonging to this family can be selected by choosing the appropriate values of the parameters . The training of a machine restricted to employ ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. A particular function belonging to this family can be selected by choosing the appropriate values of the parameters . The training of a machine restricted to employ ...
Sivu 37
... function family is of the form ( 2.36 ) g ( X ) = · w1fi ( X ) + • · + wMfм ( X ) + WM + 1 that is , a i ( 2 · 37 ) function family . We shall assume that the functions fi ( X ) , ¿ = 1 , . . . , M , are such that the loci of points in ...
... function family is of the form ( 2.36 ) g ( X ) = · w1fi ( X ) + • · + wMfм ( X ) + WM + 1 that is , a i ( 2 · 37 ) function family . We shall assume that the functions fi ( X ) , ¿ = 1 , . . . , M , are such that the loci of points in ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
LAYERED MACHINES | 95 |
Tekijänoikeudet | |
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assume belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X gi(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vector Stanford subsets X1 subsidiary discriminant functions Suppose terns training patterns training sequence training set training subsets transformation two-layer machine values W₁ wa+1 weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |