Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 34
Sivu 3
... classifier a device with d input lines and one output line ( see Fig . 1.1 ) . The d input lines are activated simultaneously by the pattern , and the output line responds * * 1 xa Pattern ( data to be classified ) ... PATTERN CLASSIFIERS 3.
... classifier a device with d input lines and one output line ( see Fig . 1.1 ) . The d input lines are activated simultaneously by the pattern , and the output line responds * * 1 xa Pattern ( data to be classified ) ... PATTERN CLASSIFIERS 3.
Sivu 7
... classifier method will produce a more detailed functional block diagram of the basic model for a pattern classifier discussed in Sec . 1.2 . Our discriminant- function pattern classifier , illustrated in Fig . 1.4 ... PATTERN CLASSIFIERS 7.
... classifier method will produce a more detailed functional block diagram of the basic model for a pattern classifier discussed in Sec . 1.2 . Our discriminant- function pattern classifier , illustrated in Fig . 1.4 ... PATTERN CLASSIFIERS 7.
Sivu 9
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. We shall assume here that little if any a ... classifier is eventually to achieve must be achieved largely by an adjustment process , which has become known as training ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. We shall assume here that little if any a ... classifier is eventually to achieve must be achieved largely by an adjustment process , which has become known as training ...
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 |