Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 12
Sivu 46
... shown that an optimum classifying machine could be achieved by computing and comparing the lx ( i ) . The computations are particularly simple if the loss function λ ( ij ) is assumed to be of the type λ ( ij ) = 1 - δι ; ( 3.5 ) i ...
... shown that an optimum classifying machine could be achieved by computing and comparing the lx ( i ) . The computations are particularly simple if the loss function λ ( ij ) is assumed to be of the type λ ( ij ) = 1 - δι ; ( 3.5 ) i ...
Sivu 103
... shown since they are the closest to the Y , pattern hyper- plane ( they make the two least - negative dot products with Y1 ) . At the next stage , examining the weight - vector positions with respect to the Y2 pattern hyperplane we see ...
... shown since they are the closest to the Y , pattern hyper- plane ( they make the two least - negative dot products with Y1 ) . At the next stage , examining the weight - vector positions with respect to the Y2 pattern hyperplane we see ...
Sivu 106
... shown in Fig . 6.7a . In this figure the points marked repre- sent patterns belonging to X1 , and the points marked O represent pat- terns belonging to X2 . Clearly the TLUS in the first layer of the desired layered machine must at ...
... shown in Fig . 6.7a . In this figure the points marked repre- sent patterns belonging to X1 , and the points marked O represent pat- terns belonging to X2 . Clearly the TLUS in the first layer of the desired layered machine must at ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges gi(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |