Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 26
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... probability - density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an assumption permitted a straightforward calculation of the ...
... probability - density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an assumption permitted a straightforward calculation of the ...
Sivu 118
... probability of error . This is so because the underlying probability distributions may be sufficiently overlapping that the optimum decision surfaces do not perfectly separate the training subsets . Consider , for example , the ...
... probability of error . This is so because the underlying probability distributions may be sufficiently overlapping that the optimum decision surfaces do not perfectly separate the training subsets . Consider , for example , the ...
Sivu 136
... probability - density function , bivariate , 50 equations of , 51 , 52 , 53 , 54 multivariate , 54 Novikoff , 92 , 93 Null category , 3 Number of linear dichotomies , 32 , 67 Okajima , 125 , 126 Optimum classifier , for binary pat ...
... probability - density function , bivariate , 50 equations of , 51 , 52 , 53 , 54 multivariate , 54 Novikoff , 92 , 93 Null category , 3 Number of linear dichotomies , 32 , 67 Okajima , 125 , 126 Optimum classifier , for binary pat ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |