Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 7
Sivu 23
... Euclidean distance from the origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wa + 1 / w | . ( If Aw > 0 , the origin is on the positive side of the hyperplane . ) The equation X. n + Aw ...
... Euclidean distance from the origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wa + 1 / w | . ( If Aw > 0 , the origin is on the positive side of the hyperplane . ) The equation X. n + Aw ...
Sivu 24
... Euclidean distance d ( X , P ; ) from an arbi- i = 1 , 9 trary point X to the point set P ; by d ( X , P ; ) = min j = 1 , ... , Li X - P ( 2.16 ) That is , the distance between X and P , is the smallest of the distances between X and ...
... Euclidean distance d ( X , P ; ) from an arbi- i = 1 , 9 trary point X to the point set P ; by d ( X , P ; ) = min j = 1 , ... , Li X - P ( 2.16 ) That is , the distance between X and P , is the smallest of the distances between X and ...
Sivu 119
... distances between a pattern to be classified and members of the training subsets . Sometimes the distance used is not simple Euclidean distance , but some function that depends on the geometric arrangement of the patterns PIECEWISE ...
... distances between a pattern to be classified and members of the training subsets . Sometimes the distance used is not simple Euclidean distance , but some function that depends on the geometric arrangement of the patterns PIECEWISE ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
LAYERED MACHINES | 95 |
Tekijänoikeudet | |
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Yleiset termit ja lausekkeet
assume belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X gi(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 vector 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 |