Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 18
Sivu 56
... covariance matrices For the optimum discriminant functions for normal patterns , expansion of Eq . ( 3.31 ) yields = -1 / 2X ' ; - 1X + X'Σ ; -1M ; gi ( X ) - – 1⁄2M¿ ¢ Σ¿ ̄1M ; + log pi - 11⁄2 log i i = 1 , • " R ( 3.32 ) In the ...
... covariance matrices For the optimum discriminant functions for normal patterns , expansion of Eq . ( 3.31 ) yields = -1 / 2X ' ; - 1X + X'Σ ; -1M ; gi ( X ) - – 1⁄2M¿ ¢ Σ¿ ̄1M ; + log pi - 11⁄2 log i i = 1 , • " R ( 3.32 ) In the ...
Sivu 58
... covariance matrix of the ith category . The ( X ) ; and ( ) ; are reasonable * estimates of M , and 2 , respectively . The use of these estimates to specify the discriminant functions would constitute a para- metric training method . i ...
... covariance matrix of the ith category . The ( X ) ; and ( ) ; are reasonable * estimates of M , and 2 , respectively . The use of these estimates to specify the discriminant functions would constitute a para- metric training method . i ...
Sivu 59
... covariance matrices , we can derive a training process which makes optimum use of the set of training patterns . In this section we shall illustrate this derivation for the case in which the covariance matrices are all known but for ...
... covariance matrices , we can derive a training process which makes optimum use of the set of training patterns . In this section we shall illustrate this derivation for the case in which the covariance matrices are all known but for ...
Sisältö
Preface vii | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume 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 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 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 |