Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 7
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 ...
... 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 ...
Sivu 55
... pattern space . A set of normal patterns would then tend to be grouped in an ellipsoidal cluster centered around a prototype pattern M. 3.8 The optimum classifier for normal patterns ... " = We are now ready to derive the optimum ...
... pattern space . A set of normal patterns would then tend to be grouped in an ellipsoidal cluster centered around a prototype pattern M. 3.8 The optimum classifier for normal patterns ... " = We are now ready to derive the optimum ...
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
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 |