Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 12
Sivu 23
... origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wa + 1 / | W | . ( If Aw > 0 , the origin is on the positive side of the hyperplane . ) The equation X. n + Aw = 0 ( 2.15 ) is said to be ...
... origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wa + 1 / | W | . ( If Aw > 0 , the origin is on the positive side of the hyperplane . ) The equation X. n + Aw = 0 ( 2.15 ) is said to be ...
Sivu 52
... origin . Contours of equal probability density ( 21220122122 +222 = constant ) are ellipses , cen- tered on the origin , whose major axes lie along the line 21 = 22. The eccentricities of the ellipses are equal to 2012 VI + 012 When σ12 ...
... origin . Contours of equal probability density ( 21220122122 +222 = constant ) are ellipses , cen- tered on the origin , whose major axes lie along the line 21 = 22. The eccentricities of the ellipses are equal to 2012 VI + 012 When σ12 ...
Sivu 105
... origin , whose vertices represent the eight possible combinations of re- sponses of three TLUS . This cube is shown in Fig . 6.66 . If we number the coordinate axes of the image - space cube in accordance with the TLU 5 TLU 3 TLU 2 2 3 ...
... origin , whose vertices represent the eight possible combinations of re- sponses of three TLUS . This cube is shown in Fig . 6.66 . If we number the coordinate axes of the image - space cube in accordance with the TLU 5 TLU 3 TLU 2 2 3 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying 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 |