Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 26
Sivu 20
... Note that the decision surfaces are segments of hyperplanes ( lines for d = 2 ) , and that S12 is redundant . In the special case in which the linear machine is a minimum - distance classi- fier , the surface Si ; is the hyperplane ...
... Note that the decision surfaces are segments of hyperplanes ( lines for d = 2 ) , and that S12 is redundant . In the special case in which the linear machine is a minimum - distance classi- fier , the surface Si ; is the hyperplane ...
Sivu 23
... Note from Fig . 2.5 that the absolute value of n⚫ P is the normal Euclidean distance from the origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wa + 1 / w ) . ( If A > 0 , the origin is ...
... Note from Fig . 2.5 that the absolute value of n⚫ P is the normal Euclidean distance from the origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wa + 1 / w ) . ( If A > 0 , the origin is ...
Sivu 39
... Note the pronounced threshold effect , for large M + 1 , around λ = 2 . Also note that for each value of M P2 ( M + 1 ) , M = 1/2 ( 2.45 ) The threshold effect around 2 ( M + 1 ) can be expressed quantitively by lim P ( 2+ ) ( M + 1 ) ...
... Note the pronounced threshold effect , for large M + 1 , around λ = 2 . Also note that for each value of M P2 ( M + 1 ) , M = 1/2 ( 2.45 ) The threshold effect around 2 ( M + 1 ) can be expressed quantitively by lim P ( 2+ ) ( M + 1 ) ...
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 |