Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 58
... sample mean ( or center of gravity ) of the ith category , and ( ) ; is called the sample covariance matrix of the ith category . The ( X ) , and ( ) ; are reasonable * estimates of M , and E , respectively . The use of these estimates ...
... sample mean ( or center of gravity ) of the ith category , and ( ) ; is called the sample covariance matrix of the ith category . The ( X ) , and ( ) ; are reasonable * estimates of M , and E , respectively . The use of these estimates ...
Sivu 60
... mean of X from u to u1 , น and the covariance matrix from + K to + K1 . Training the classi- fying machine on the pattern X1 is accomplished by modifying the dis- criminant ... sample mean = X ; N ( 3.50 60 PARAMETRIC TRAINING METHODS.
... mean of X from u to u1 , น and the covariance matrix from + K to + K1 . Training the classi- fying machine on the pattern X1 is accomplished by modifying the dis- criminant ... sample mean = X ; N ( 3.50 60 PARAMETRIC TRAINING METHODS.
Sivu 61
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. and the sample mean = X ; N ( 3.50 ) The optimum a posteriori discriminant function ( after training on the set { X1 , X2 , . . . , XN ) is then given by Eq . ( 3 · 31 ) ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. and the sample mean = X ; N ( 3.50 ) The optimum a posteriori discriminant function ( after training on the set { X1 , X2 , . . . , XN ) is then given by Eq . ( 3 · 31 ) ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X 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 partition pattern classifier pattern hyperplane pattern space pattern vector 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 |