Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 4
... consist of a finite set of real numbers . 1.3 The problem of what to measure In assuming that the data to be classified consist of d real numbers , we are obliged to mention , at least briefly , the difficulties that attend selecting ...
... consist of a finite set of real numbers . 1.3 The problem of what to measure In assuming that the data to be classified consist of d real numbers , we are obliged to mention , at least briefly , the difficulties that attend selecting ...
Sivu 98
... consists of the vote - taking TLU whose response is the majority response of the committee TLUs . A committee machine of size P is depicted in Fig . 6.4 . The committee machine can be generalized by allowing the com- mittee TLUS to have ...
... consists of the vote - taking TLU whose response is the majority response of the committee TLUs . A committee machine of size P is depicted in Fig . 6.4 . The committee machine can be generalized by allowing the com- mittee TLUS to have ...
Sivu 115
... consists of R discriminators , where R is the number of pattern categories . Each discriminator employs a number of subsidiary linear dis- criminant functions . Thus a PWL machine consists of R banks of sub- sidiary discriminators ...
... consists of R discriminators , where R is the number of pattern categories . Each discriminator employs a number of subsidiary linear dis- criminant functions . Thus a PWL machine consists of R banks of sub- sidiary discriminators ...
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 |