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 apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |