Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 24
Sivu 70
... correction increment , which will be discussed more fully below . There are several types of error - correction procedures . We shall men- tion three of them here . These differ solely in the interpretation to be given to the value of ...
... correction increment , which will be discussed more fully below . There are several types of error - correction procedures . We shall men- tion three of them here . These differ solely in the interpretation to be given to the value of ...
Sivu 81
... correction increment c is clearly unimportant so long as it is positive . If the theorem were true for c = 1 , it ... correction procedure instead of the fixed - increment error - correction pro- cedure . In the absolute error ...
... correction increment c is clearly unimportant so long as it is positive . If the theorem were true for c = 1 , it ... correction procedure instead of the fixed - increment error - correction pro- cedure . In the absolute error ...
Sivu 119
... correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision surface , an error - correction procedure could never stabilize at the optimum surface since inevitable errors would ...
... correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision surface , an error - correction procedure could never stabilize at the optimum surface since inevitable errors would ...
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 |