Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu 8
... element whose threshold value is equal to zero . For this reason the threshold element assumes an important role in pattern - classifying machines . We shall use the block diagram of Fig . 1.5 as a basic model of two - category pattern ...
... element whose threshold value is equal to zero . For this reason the threshold element assumes an important role in pattern - classifying machines . We shall use the block diagram of Fig . 1.5 as a basic model of two - category pattern ...
Sivu 21
... element , is called a threshold logic unit ( TLU ) . We shall ordinarily assume that the threshold element is a device which responds with a +1 signal if g ( X ) > 0 and a −1 signal if g ( X ) < 0. We must then associate a TLU output ...
... element , is called a threshold logic unit ( TLU ) . We shall ordinarily assume that the threshold element is a device which responds with a +1 signal if g ( X ) > 0 and a −1 signal if g ( X ) < 0. We must then associate a TLU output ...
Sivu 48
... element w d Summing device X : Pattern +1 10 d + 1 Weights FIGURE 3.1 The optimum classifier for binary patterns whose components are statistically independent linear in this case . Therefore a TLU can be used as the optimum classifying ...
... element w d Summing device X : Pattern +1 10 d + 1 Weights FIGURE 3.1 The optimum classifier for binary patterns whose components are statistically independent linear in this case . Therefore a TLU can be used as the optimum classifying ...
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 |