Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 15
... ranges , is called a family of functions . A particular function belonging to this family can be selected 15 SOME IMPORTANT DISCRIMINANT FUNCTIONS: THEIR PROPERTIES AND THEIR IMPLEMENTATIONS Families of discriminant functions,
... ranges , is called a family of functions . A particular function belonging to this family can be selected 15 SOME IMPORTANT DISCRIMINANT FUNCTIONS: THEIR PROPERTIES AND THEIR IMPLEMENTATIONS Families of discriminant functions,
Sivu 16
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. A particular function belonging to this family can be selected by choosing the appropriate values of the parameters . The training of a machine restricted to employ ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. A particular function belonging to this family can be selected by choosing the appropriate values of the parameters . The training of a machine restricted to employ ...
Sivu 37
... function family is of the form ... • g ( X ) w1f1 ( X ) + ・・・ + WMƒM ( X ) + WM + 1 1 ( 2 · 37 ) that is , a function family . We shall assume that the functions fi ( X ) , i = 1 , M , are such that the loci of points in the pattern ...
... function family is of the form ... • g ( X ) w1f1 ( X ) + ・・・ + WMƒM ( X ) + WM + 1 1 ( 2 · 37 ) that is , a function family . We shall assume that the functions fi ( X ) , i = 1 , M , are such that the loci of points in the pattern ...
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 step subsidiary discriminant Suppose terns 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 |