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 belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |