Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 28
Sivu 9
... X2 . The coordinates of the points X1 and X2 constitute the parameters of the pat- tern sets . The exact values of the coordinates of the points X1 and X2 are not known , however . If they were known , it might be reasonable for the ...
... X2 . The coordinates of the points X1 and X2 constitute the parameters of the pat- tern sets . The exact values of the coordinates of the points X1 and X2 are not known , however . If they were known , it might be reasonable for the ...
Sivu 20
... ( X1 , X2 , , XN } , N in number . Let the patterns of X be classified in such a way that each pattern in X belongs to only one of R categories . This classification divides I into the subsets X1 , X2 , to category i for i = 1 , • , XR ...
... ( X1 , X2 , , XN } , N in number . Let the patterns of X be classified in such a way that each pattern in X belongs to only one of R categories . This classification divides I into the subsets X1 , X2 , to category i for i = 1 , • , XR ...
Sivu 50
... x1 and x2 . Let the mean values of x1 and x2 be equal to m1 and m2 , respectively . Let the variances of x and x2 be equal to σ11 and σ22 , respectively . * Using E [ ] to denote the expectation operator , we then have E [ x1 ] = mi E ...
... x1 and x2 . Let the mean values of x1 and x2 be equal to m1 and m2 , respectively . Let the variances of x and x2 be equal to σ11 and σ22 , respectively . * Using E [ ] to denote the expectation operator , we then have E [ x1 ] = mi E ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
2 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |