Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 9
Sivu 37
... will illustrate the use of the above expression : 1. ( X ) is a special quadric function of the form + ( X ) = - | X — W│2 — a2 - ( 2.39 ) Here , ( X ) = 0 defines a hypersphere SOME IMPORTANT DISCRIMINANT FUNCTIONS 37.
... will illustrate the use of the above expression : 1. ( X ) is a special quadric function of the form + ( X ) = - | X — W│2 — a2 - ( 2.39 ) Here , ( X ) = 0 defines a hypersphere SOME IMPORTANT DISCRIMINANT FUNCTIONS 37.
Sivu 38
... hypersphere , where W is the center of the hyper- sphere and a is its radius . Expanding the above equation yields Þ ... hypersphere ( N , d ) = L ( N , d + 1 ) ( 2.41 ) The above expression assumes , of course , that the points in X are ...
... hypersphere , where W is the center of the hyper- sphere and a is its radius . Expanding the above equation yields Þ ... hypersphere ( N , d ) = L ( N , d + 1 ) ( 2.41 ) The above expression assumes , of course , that the points in X are ...
Sivu 92
... hypersphere S ( W ) , centered at W and with some radius 1 ( W ) . But the preceding statement is true for all W in W. Therefore , Ŵ must converge to one of the points defined by the joint intersection of all hyperspheres S ( W ) for ...
... hypersphere S ( W ) , centered at W and with some radius 1 ( W ) . But the preceding statement is true for all W in W. Therefore , Ŵ must converge to one of the points defined by the joint intersection of all hyperspheres S ( W ) for ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X 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 |