Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 34
Sivu 6
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. the decision surfaces divide Ed into R regions which we shall call decision regions . The ith region R ; is the set of points which map into the ith cate- gory number ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. the decision surfaces divide Ed into R regions which we shall call decision regions . The ith region R ; is the set of points which map into the ith cate- gory number ...
Sivu 28
... decision surfaces The decision surfaces of quadric machines are sections of second - degree surfaces which we shall call quadric surfaces . Specifically , if R , and R , share a common boundary , it is a section of the surface S1 ...
... decision surfaces The decision surfaces of quadric machines are sections of second - degree surfaces which we shall call quadric surfaces . Specifically , if R , and R , share a common boundary , it is a section of the surface S1 ...
Sivu 119
... decision surface which minimizes the probability of error is a hyper- plane perpendicular to the line segment joining the means of the two density functions . It should be observed that if the two density func- tions overlap ...
... decision surface which minimizes the probability of error is a hyper- plane perpendicular to the line segment joining the means of the two density functions . It should be observed that if the two density func- tions overlap ...
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 |