Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 52
... pattern points in a two - dimensional space . Such patterns will be called bivariate normal patterns . They will ... prototype pattern for that category . As the number of sample points in a cluster increases , the coordinates of the ...
... pattern points in a two - dimensional space . Such patterns will be called bivariate normal patterns . They will ... prototype pattern for that category . As the number of sample points in a cluster increases , the coordinates of the ...
Sivu 116
... prototype " or typical pattern around which all other patterns in the category cluster . We shall say in this case that there is more than one mode per category . Suppose that there are L ; prototype patterns for the ith category and ...
... prototype " or typical pattern around which all other patterns in the category cluster . We shall say in this case that there is more than one mode per category . Suppose that there are L ; prototype patterns for the ith category and ...
Sivu 136
... Pattern , prototype , 18 , 52 Pattern sets , linearly separable , 20 linearly contained . 82 Pattern space , 5 Pattern vector , 5 augmented , 66 Patterns , normal , 52 , 54 with independent binary compo- nents , 47 Perceptron ...
... Pattern , prototype , 18 , 52 Pattern sets , linearly separable , 20 linearly contained . 82 Pattern space , 5 Pattern vector , 5 augmented , 66 Patterns , normal , 52 , 54 with independent binary compo- nents , 47 Perceptron ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS 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 partition pattern classifier pattern hyperplane pattern space pattern vector 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 TLU response 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 |