Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 26
Sivu 75
... correction training procedure for R > 2 A linear machine for classifying patterns belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and a maximum selector ( Fig . 2-1 ) . Each ...
... correction training procedure for R > 2 A linear machine for classifying patterns belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and a maximum selector ( Fig . 2-1 ) . Each ...
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 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted assume augmented pattern belonging to category binary called Chapter cluster committee machine components Cornell Aeronautical Laboratory correction increment covariance matrix d-dimensional decision regions decision surfaces denote density function discussed dot products equal error-correction procedure Euclidean distance example Fix and Hodges fixed-increment error-correction function family g₁(X gi(X given hypersphere image-space implemented initial weight vectors layered machine linear dichotomies linear discriminant functions linearly separable loss function Lx(i mean vector minimum-distance classifier number of linear number of patterns optimum classifier parameters partition pattern classifier pattern hyperplane pattern points pattern space pattern vector pattern-classifying machines patterns belonging Perceptron piecewise linear point sets positive probability distributions prototype pattern PWL machine quadratic form quadric discriminant function quadric function sample covariance matrix solution weight vector Stanford subsets X1 Suppose training patterns training sequence training set training subsets values W₁ wa+1 weight point weight space X₁ X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |