Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 20
Sivu 20
... exist such that · • ? gi ( X ) > gi ( X ) j = 1 , ... , R , ji for all X in Xi for all i = ང R ( 2.9 ) . " = 2. We say ... exists which has each member of X1 on one side and each member of X2 on the other side . 1 Because the decision ...
... exist such that · • ? gi ( X ) > gi ( X ) j = 1 , ... , R , ji for all X in Xi for all i = ང R ( 2.9 ) . " = 2. We say ... exists which has each member of X1 on one side and each member of X2 on the other side . 1 Because the decision ...
Sivu 84
... exists . ( A similar proof can be given for arbitrary Ŵ1 . ) But , since every pattern in y occurs infinitely often in the training se- quence , termination can occur only if a solution vector is found , which proves the theorem . Other ...
... exists . ( A similar proof can be given for arbitrary Ŵ1 . ) But , since every pattern in y occurs infinitely often in the training se- quence , termination can occur only if a solution vector is found , which proves the theorem . Other ...
Sivu 87
... exists . 5.5 A training theorem for R - category linear machines Suppose we are given R finite subsets of augmented training pattern vec- tors Y1 , Y2 , ... , YR . These subsets are linearly separable if and only if there exist R ...
... exists . 5.5 A training theorem for R - category linear machines Suppose we are given R finite subsets of augmented training pattern vec- tors Y1 , Y2 , ... , YR . These subsets are linearly separable if and only if there exist R ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components Computer 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 terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |