Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 20
Sivu 53
... distribution . The notation used in Eq . ( 3 · 20 ) to describe the normal distribution can be made more compact if we define and use the following matrices . Let the pattern vector X be a column vector ( a 2 × 1 matrix ) with compo ...
... distribution . The notation used in Eq . ( 3 · 20 ) to describe the normal distribution can be made more compact if we define and use the following matrices . Let the pattern vector X be a column vector ( a 2 × 1 matrix ) with compo ...
Sivu 54
... distribution which describes the joint probability density of d components . Patterns selected according to this joint proba- bility distribution will be called multivariate normal patterns or , more simply , normal patterns . The ...
... distribution which describes the joint probability density of d components . Patterns selected according to this joint proba- bility distribution will be called multivariate normal patterns or , more simply , normal patterns . The ...
Sivu 123
... distribution of which the points are samples . It is true that if the probability distribution has only one mode ( uni- modal ) , then the center of gravity of a set of points is often a good esti- mate for this mode . In multimodal ...
... distribution of which the points are samples . It is true that if the probability distribution has only one mode ( uni- modal ) , then the center of gravity of a set of points is often a good esti- mate for this mode . In multimodal ...
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 |