Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 46
Sivu 9
... values are unknown . If the values of these parameters were known , ade- quate discriminant functions based on them could be directly specified . In the parametric training methods the training set is used for the purpose of obtaining ...
... values are unknown . If the values of these parameters were known , ade- quate discriminant functions based on them could be directly specified . In the parametric training methods the training set is used for the purpose of obtaining ...
Sivu 44
... values might also be unknown , are the a priori probabilities for each class p ( i ) , i = 1 , . . . , R. In such a ... values of the parameters of the p ( X | i ) and of the parameters p ( i ) . 2. The values of the parameters of the p ...
... values might also be unknown , are the a priori probabilities for each class p ( i ) , i = 1 , . . . , R. In such a ... values of the parameters of the p ( X | i ) and of the parameters p ( i ) . 2. The values of the parameters of the p ...
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 |
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 decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges 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 second layer shown in Fig solution weight vectors 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 |