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
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted assume augmented pattern belonging to category binary called Chapter cluster committee machine components Cornell Aeronautical Laboratory correction increment covariance matrix d-dimensional decision regions decision surfaces denote density function discussed dot products equal error-correction procedure Euclidean distance example Fix and Hodges fixed-increment error-correction function family g₁(X gi(X given hypersphere image-space implemented initial weight vectors layered machine linear dichotomies linear discriminant functions linearly separable loss function Lx(i mean vector minimum-distance classifier number of linear number of patterns optimum classifier parameters partition pattern classifier pattern hyperplane pattern points pattern space pattern vector pattern-classifying machines patterns belonging Perceptron piecewise linear point sets positive probability distributions prototype pattern PWL machine quadratic form quadric discriminant function quadric function sample covariance matrix solution weight vector Stanford subsets X1 Suppose training patterns training sequence training set training subsets values W₁ wa+1 weight point weight space X₁ X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |