Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 9
Sivu 32
... partitioned by a ( d — 1 ) -dimensional hyperplane . ( For each distinct partition , there are two different classifications ) . Before obtaining a general expression for L ( N , d ) consider the case N = 4 , d = 2 as an example ...
... partitioned by a ( d — 1 ) -dimensional hyperplane . ( For each distinct partition , there are two different classifications ) . Before obtaining a general expression for L ( N , d ) consider the case N = 4 , d = 2 as an example ...
Sivu 34
... partitions X ' and suppose that H ; can be made to pass through Xy without altering the partition of X ' . The hyperplane H ; can now be moved to one of two positions with respect to Xx , still without alter- ing the partition of X ...
... partitions X ' and suppose that H ; can be made to pass through Xy without altering the partition of X ' . The hyperplane H ; can now be moved to one of two positions with respect to Xx , still without alter- ing the partition of X ...
Sivu 108
... partition shown in Fig . 6.7a is also nonredundant . A nonredundant partition is not necessarily one that uses a minimum number of hyperplanes , however . Thus in Fig . 6 · 8a , one hyperplane ( line ) would suffice to partition the ...
... partition shown in Fig . 6.7a is also nonredundant . A nonredundant partition is not necessarily one that uses a minimum number of hyperplanes , however . Thus in Fig . 6 · 8a , one hyperplane ( line ) would suffice to partition the ...
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 |