Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 42
Sivu 75
... weight vector with an augmented pattern vector ; that is , gi ( X ) = W ( i ) ... vectors so that it responds correctly to every pattern in y . A response to ... weight vectors when they exist . In this procedure , each pattern in ...
... weight vector with an augmented pattern vector ; that is , gi ( X ) = W ( i ) ... vectors so that it responds correctly to every pattern in y . A response to ... weight vectors when they exist . In this procedure , each pattern in ...
Sivu 101
... vector to ( or from ) the weight vector . Thus , if Y causes a majority of the committee TLUS to respond negatively , we adjust the 2 ( | N | + 1 ) weight vectors making the least- negative ( but not positive ) dot products with Y. If the ...
... vector to ( or from ) the weight vector . Thus , if Y causes a majority of the committee TLUS to respond negatively , we adjust the 2 ( | N | + 1 ) weight vectors making the least- negative ( but not positive ) dot products with Y. If the ...
Sivu 103
... weight vectors . At this stage the training process terminates . 1 This example can also be used to illustrate the necessity for begin- ning with initial weight vectors of approximately the same length . Sup- pose that W2 ( 1 ) were ...
... weight vectors . At this stage the training process terminates . 1 This example can also be used to illustrate the necessity for begin- ning with initial weight vectors of approximately the same length . Sup- pose that W2 ( 1 ) were ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix d-dimensional 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 subsets X1 subsidiary discriminant functions Suppose terns training patterns training sequence training set training subsets transformation two-layer machine values W₁ 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 |