Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 35
Sivu 52
... equal probability density ( z12-20122122 + 222 = constant ) are ellipses , cen- tered on the origin , whose major axes lie along the line z1 = 22. The eccentricities of the ellipses are equal to 2012 VI + 1012 When σ12 is zero , the ...
... equal probability density ( z12-20122122 + 222 = constant ) are ellipses , cen- tered on the origin , whose major axes lie along the line z1 = 22. The eccentricities of the ellipses are equal to 2012 VI + 1012 When σ12 is zero , the ...
Sivu 58
... equal to rank Q.Q ' , which is equal to rank Qi , and since rank Q ; min ( d , N1 ) , rank ( Σ ) ; < d if N ; < d . If N ; d , Q ; will have rank equal to d if and only if there are no linear dependencies among the rows of Q. Or ...
... equal to rank Q.Q ' , which is equal to rank Qi , and since rank Q ; min ( d , N1 ) , rank ( Σ ) ; < d if N ; < d . If N ; d , Q ; will have rank equal to d if and only if there are no linear dependencies among the rows of Q. Or ...
Sivu 89
... equal to 1 , . . . , R , j # i . = = 1 , 9 R , ji , let the jth block of D Y for j 4. For each Z ; \ ; ( Y ) , j components be set equal to -Y . Y = 5. Let all other components of each Z ; \ ; ( Y ) be set equal to zero . The above ...
... equal to 1 , . . . , R , j # i . = = 1 , 9 R , ji , let the jth block of D Y for j 4. For each Z ; \ ; ( Y ) , j components be set equal to -Y . Y = 5. Let all other components of each Z ; \ ; ( Y ) be set equal to zero . The above ...
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 |