Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 21
Sivu 18
... distance classifier is a linear machine . Suppose that the components of P ; are pil , Piz , . . . , Pid . Then the linear machine of Fig . 2.1 is a minimum - distance classifier with respect to the points P1 , P2 , . , PR if the ...
... distance classifier is a linear machine . Suppose that the components of P ; are pil , Piz , . . . , Pid . Then the linear machine of Fig . 2.1 is a minimum - distance classifier with respect to the points P1 , P2 , . , PR if the ...
Sivu 24
... distance d ( X , P ; ) from an arbi- trary point X to the point set P ; by d ( X , Pi ) = min j = 1 , ... , Li X - − P¿ ‹ 3 ) | ( 2.16 ) That is , the distance between X and P ; is the smallest of the distances between X and each point ...
... distance d ( X , P ; ) from an arbi- trary point X to the point set P ; by d ( X , Pi ) = min j = 1 , ... , Li X - − P¿ ‹ 3 ) | ( 2.16 ) That is , the distance between X and P ; is the smallest of the distances between X and each point ...
Sivu 121
... distance to each of the modes and place it in that category having the nearest mode . But this procedure is just a minimum - distance - classification rule with respect to point sets . The points belonging to the ith point set P ; are ...
... distance to each of the modes and place it in that category having the nearest mode . But this procedure is just a minimum - distance - classification rule with respect to point sets . The points belonging to the ith point set P ; are ...
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 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 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 |