Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 16
... values of the parameters . The training of a machine restricted to employ discriminant functions belonging to a particular family can then be accomplished by adjusting the values of the parame- ters . We shall often call these ...
... values of the parameters . The training of a machine restricted to employ discriminant functions belonging to a particular family can then be accomplished by adjusting the values of the parame- ters . We shall often call these ...
Sivu 44
... values might also be unknown , are the a priori probabilities for each class p ( i ) , i = 1 , ܝ ܂ ܂ R. In such a ... values of the parameters of the p ( Xi ) and of the parameters p ( i ) . 2. The values of the parameters of ...
... values might also be unknown , are the a priori probabilities for each class p ( i ) , i = 1 , ܝ ܂ ܂ R. In such a ... values of the parameters of the p ( Xi ) and of the parameters p ( i ) . 2. The values of the parameters of ...
Sivu 49
... values of the a priori proba- bilities p ( 1 ) and 1 p ( 1 ) affect only the value of wa + 1 . As category 1 becomes less probable a priori , wa + 1 decreases . Such a decrease of wa + 1 favors a category 2 response for all patterns ...
... values of the a priori proba- bilities p ( 1 ) and 1 p ( 1 ) affect only the value of wa + 1 . As category 1 becomes less probable a priori , wa + 1 decreases . Such a decrease of wa + 1 favors a category 2 response for all patterns ...
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 |