Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 49
... probability values . These optimum estimates are meaningful , however , only when the unknown proba- bility values are themselves random variables with known probability distributions . As an example , consider the case of N successive ...
... probability values . These optimum estimates are meaningful , however , only when the unknown proba- bility values are themselves random variables with known probability distributions . As an example , consider the case of N successive ...
Sivu 50
... values of the TLU weights and threshold . 3.6 The bivariate normal probability - density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an ...
... values of the TLU weights and threshold . 3.6 The bivariate normal probability - density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an ...
Sivu 52
... variables x and x2 is more complicated * than that of Eq . ( 3.18 ) , but the general properties of the function are ... random variables . The assumption made in the previous footnote should now be generalized to σ122011022 . center of ...
... variables x and x2 is more complicated * than that of Eq . ( 3.18 ) , but the general properties of the function are ... random variables . The assumption made in the previous footnote should now be generalized to σ122011022 . center 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 |