Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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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 ... DISCRIMINANT FUNCTIONS Quadric decision surfaces, Implementation of quadric discriminant ...
... 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 ... DISCRIMINANT FUNCTIONS Quadric decision surfaces, Implementation of quadric discriminant ...
Sivu 55
... quadratic form . The surfaces defined by setting this quadratic form equal ... function p ( Xi ) that apply . That is , we know R normal ... discriminant functions for PARAMETRIC TRAINING METHODS 55 The optimum classifier for normal patterns,
... quadratic form . The surfaces defined by setting this quadratic form equal ... function p ( Xi ) that apply . That is , we know R normal ... discriminant functions for PARAMETRIC TRAINING METHODS 55 The optimum classifier for normal patterns,
Sivu 127
... DISCRIMINANT FUNCTIONS A⚫1 Separation of a 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 ...
... DISCRIMINANT FUNCTIONS A⚫1 Separation of a 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 ...
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 g₁(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 second layer shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns 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 |