Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 17
Sivu 80
... sequence of weight vectors Sw W1 , W2 , W 3 , Wk , . . . such that , for some index ko , W ko 1 = • = Wko + 1 = Wko + 2 =・・・ satisfies inequalities ( 5.1 ) . The initial weight vector W1 is arbitrary . We shall be interested here in ...
... sequence of weight vectors Sw W1 , W2 , W 3 , Wk , . . . such that , for some index ko , W ko 1 = • = Wko + 1 = Wko + 2 =・・・ satisfies inequalities ( 5.1 ) . The initial weight vector W1 is arbitrary . We shall be interested here in ...
Sivu 91
... weight vector W such that W.Y > 0 for each Y in y ' . Let W be the set of solution weight vectors . As before , W is an open convex polyhedral cone . = Y1 ' , Y2 ' , Let Sy Y ' , be a training sequence ... weight - vector sequence Sw as ...
... weight vector W such that W.Y > 0 for each Y in y ' . Let W be the set of solution weight vectors . As before , W is an open convex polyhedral cone . = Y1 ' , Y2 ' , Let Sy Y ' , be a training sequence ... weight - vector sequence Sw as ...
Sivu 93
... sequence which is generated recursively from the weight - vector sequence . The ( k + 1 ) th pattern in the training sequence is that mem- ber of y ' which has the smallest ( most - negative ) dot product with Wx + 1 . They also show ...
... sequence which is generated recursively from the weight - vector sequence . The ( k + 1 ) th pattern in the training sequence is that mem- ber of y ' which has the smallest ( most - negative ) dot product with Wx + 1 . They also show ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components 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 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 vectors subsets X1 subsidiary discriminant functions Suppose terns 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 |