Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 19
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 ... METHODS FOR MACHINES 75 An error-correction training procedure for R > 2,
... 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 ... METHODS FOR MACHINES 75 An error-correction training procedure for R > 2,
Sivu 81
... error- correction procedure instead of the fixed - increment error - correction pro- cedure . In the absolute error - correction procedure , the value of c is taken to be the smallest integer for which cY Yk > W Yk . With this pro ...
... error- correction procedure instead of the fixed - increment error - correction pro- cedure . In the absolute error - correction procedure , the value of c is taken to be the smallest integer for which cY Yk > W Yk . With this pro ...
Sivu 119
... error is a hyper- plane perpendicular to the line segment joining the means of the two density functions . It should ... correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision ...
... error is a hyper- plane perpendicular to the line segment joining the means of the two density functions . It should ... correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision ...
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 |