Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 7
Sivu 28
... quadratic form are called positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic form will never be negative , and it and A are called positive semidefinite . 2.9 ...
... quadratic form are called positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic form will never be negative , and it and A are called positive semidefinite . 2.9 ...
Sivu 55
... quadratic form . The surfaces defined by setting this quadratic form equal to constants are hyperellipsoids centered on the point M. These ellipsoids are surfaces of equal probability in the d - dimen- sional pattern space . A set of ...
... quadratic form . The surfaces defined by setting this quadratic form equal to constants are hyperellipsoids centered on the point M. These ellipsoids are surfaces of equal probability in the d - dimen- sional pattern space . A set of ...
Sivu 127
... quadratic form into positive and negative parts Consider the quadric function g ( X ) = X'AX + B'X + C ( A - 1 ) where A is a real , d X d , symmetric matrix , B is a d - dimensional column ... quadratic form into positive negative parts,
... quadratic form into positive and negative parts Consider the quadric function g ( X ) = X'AX + B'X + C ( A - 1 ) where A is a real , d X d , symmetric matrix , B is a d - dimensional column ... quadratic form into positive negative parts,
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume belonging to category cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(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 point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors subsets X1 subsidiary discriminant functions Suppose terns TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ wa+1 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 |