Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 26
Sivu 9
... X1 , and that the pattern points in category 2 tend to cluster close to another cluster point X2 . The coordinates of the points X1 and X2 constitute the parameters of the pat- tern sets . The exact values of the coordinates of the points ...
... X1 , and that the pattern points in category 2 tend to cluster close to another cluster point X2 . The coordinates of the points X1 and X2 constitute the parameters of the pat- tern sets . The exact values of the coordinates of the points ...
Sivu 20
... { X1 , X2 , , XN } , N in number . Let the patterns of X be classified in such a way that each pattern in X belongs to only one of R categories . This classification divides X into the subsets X1 , X2 , , XR such that each pattern in X ...
... { X1 , X2 , , XN } , N in number . Let the patterns of X be classified in such a way that each pattern in X belongs to only one of R categories . This classification divides X into the subsets X1 , X2 , , XR such that each pattern in X ...
Sivu 104
... X1 and X2 . - Given training subsets X1 and X2 , the training problem for layered machines can then be viewed as a problem of adjusting the various layers of weights such that the transformations implemented by the first N − 1 layers ...
... X1 and X2 . - Given training subsets X1 and X2 , the training problem for layered machines can then be viewed as a problem of adjusting the various layers of weights such that the transformations implemented by the first N − 1 layers ...
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 |