Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 58
Sivu 56
... given by the values of the components of the transformed mean vector , ( d + 1 ) th weight is given by the value of the constant , = If R 2 , and if Σ1 g ( X ) can be written as g ( X ) = ΧΣ - ' ( Μι - log pi1⁄2M ; Σ - 1M ; -1M ;; the ...
... given by the values of the components of the transformed mean vector , ( d + 1 ) th weight is given by the value of the constant , = If R 2 , and if Σ1 g ( X ) can be written as g ( X ) = ΧΣ - ' ( Μι - log pi1⁄2M ; Σ - 1M ; -1M ;; the ...
Sivu 66
... given in terms of the weight - space representation . • 9 Wa , Wa + 1 . Suppose that the TLU has d + 1 weights , W1 , W2 , This set of weights can be represented by a point in a ( d + 1 ) -dimensional weight space . The rectangular ...
... given in terms of the weight - space representation . • 9 Wa , Wa + 1 . Suppose that the TLU has d + 1 weights , W1 , W2 , This set of weights can be represented by a point in a ( d + 1 ) -dimensional weight space . The rectangular ...
Sivu 121
... given the training subsets . Suppose the modes for the various categories , as established by a training procedure , are given by the points P. ) for i = 1 , 1 , . . . , R and j = 1 , , L. That is , there are Li typical patterns ...
... given the training subsets . Suppose the modes for the various categories , as established by a training procedure , are given by the points P. ) for i = 1 , 1 , . . . , R and j = 1 , , L. That is , there are Li typical 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 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 |