Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 8
Sivu 98
... machine . In general , a committee machine could have P committee TLUS with weight vectors W1 , .. WP , where P is odd . Such a machine is said to be of size P. These P committee TLUS comprise the first layer of a two- layer machine ...
... machine . In general , a committee machine could have P committee TLUS with weight vectors W1 , .. WP , where P is odd . Such a machine is said to be of size P. These P committee TLUS comprise the first layer of a two- layer machine ...
Sivu 103
... two least - negative dot products with Y1 ) . At the next stage , examining ... layer TLUS as voters in a " committee " is a productive representation for ... machine shown in Fig . 6.2 . The binary outputs of the first layer of ...
... two least - negative dot products with Y1 ) . At the next stage , examining ... layer TLUS as voters in a " committee " is a productive representation for ... machine shown in Fig . 6.2 . The binary outputs of the first layer of ...
Sivu 113
... 2 Widrow , 3 Brain et al . , and others . The simple a perceptron proposed by Rosenblatt is a two - layer machine consisting of a first layer of fixed TLUs followed by a single trainable TLU in the second layer . ( Rosenblatt speaks of ...
... 2 Widrow , 3 Brain et al . , and others . The simple a perceptron proposed by Rosenblatt is a two - layer machine consisting of a first layer of fixed TLUs followed by a single trainable TLU in the second layer . ( Rosenblatt speaks of ...
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 |