Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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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 X into the subsets X1 , X2 , .. , XR such that each ...
... ( 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 X into the subsets X1 , X2 , .. , XR such that each ...
Sivu 104
... X1 and X2 . - - Given training subsets X1 and X2 , the training problem for layered machines can then be viewed as a problem of adjusting the various layers of weights such that the transformations implemented by the first N - 1 layers ...
... X1 and X2 . - - Given training subsets X1 and X2 , the training problem for layered machines can then be viewed as a problem of adjusting the various layers of weights such that the transformations implemented by the first N - 1 layers ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |