Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 81
... proved quite simply as a result of Theorem 5.1 . In the modified theorem we use an absolute error- correction procedure instead of the fixed - increment error - correction pro- cedure . In the absolute error - correction procedure , the ...
... proved quite simply as a result of Theorem 5.1 . In the modified theorem we use an absolute error- correction procedure instead of the fixed - increment error - correction pro- cedure . In the absolute error - correction procedure , the ...
Sivu 84
... proved ( for Ŵ1 0 ) that the fixed - increment error - correction procedure must terminate after at most km steps if ... proving the theorem , the bound itself is not very useful in estimating how many steps will be required in a given ...
... proved ( for Ŵ1 0 ) that the fixed - increment error - correction procedure must terminate after at most km steps if ... proving the theorem , the bound itself is not very useful in estimating how many steps will be required in a given ...
Sivu 93
... proved a generalized version of Theo- rem 5.1 in which the correction increment ck of Eq . ( 5-4 ) need not be independent of k . Theorem 5.2 was first proved by C. Kesler at Cornell University . Our proof is a version of Kesler's as it ...
... proved a generalized version of Theo- rem 5.1 in which the correction increment ck of Eq . ( 5-4 ) need not be independent of k . Theorem 5.2 was first proved by C. Kesler at Cornell University . Our proof is a version of Kesler's as it ...
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 |