Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 72
... ( solution ) region . In this simple example , a solution occurs after five adjustments . Note that an adjustment to correct the response for one pattern may very well undo a correction made on a previous pattern . Eventually , however ...
... ( solution ) region . In this simple example , a solution occurs after five adjustments . Note that an adjustment to correct the response for one pattern may very well undo a correction made on a previous pattern . Eventually , however ...
Sivu 84
... solution vector exists . ( A similar proof can be given for arbitrary Ŵ1 . ) But , since every pattern in y occurs infinitely often in the training se- quence , termination can occur only if a solution vector is found , which proves the ...
... solution vector exists . ( A similar proof can be given for arbitrary Ŵ1 . ) But , since every pattern in y occurs infinitely often in the training se- quence , termination can occur only if a solution vector is found , which proves the ...
Sivu 86
... Solution region , W “ Insulated ” region , W ' • Y W. Y2 = ( M + b ) — W.Y = 0 2 FIGURE 5.1 A solution region W and an insulated region W as used in proof 2 of Theorem 5 · 1 Ŵ + Ŷ and using Eq . ( 5.23 ) we then Employing the fact that ...
... Solution region , W “ Insulated ” region , W ' • Y W. Y2 = ( M + b ) — W.Y = 0 2 FIGURE 5.1 A solution region W and an insulated region W as used in proof 2 of Theorem 5 · 1 Ŵ + Ŷ and using Eq . ( 5.23 ) we then Employing the fact that ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(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 partition pattern classifier pattern hyperplane pattern space pattern vector 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 vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ 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 |