Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu 58
... sample mean ( or center of gravity ) of the ith category , and ( E ) ; is called the sample covariance matrix of the ith category . The ( X ) ; and ( ) ; are reasonable * estimates of M , and 2 , respectively . The use of these ...
... sample mean ( or center of gravity ) of the ith category , and ( E ) ; is called the sample covariance matrix of the ith category . The ( X ) ; and ( ) ; are reasonable * estimates of M , and 2 , respectively . The use of these ...
Sivu 61
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. and the sample mean N ( X ) = Σ X ; ( 3.50 ) The optimum a posteriori discriminant function ( after training on the set { X1 , X2 , . . . , XN ) is then given by Eq ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. and the sample mean N ( X ) = Σ X ; ( 3.50 ) The optimum a posteriori discriminant function ( after training on the set { X1 , X2 , . . . , XN ) is then given by Eq ...
Sivu 123
... mean of a distribution . The sample mean or center of gravity of a set of points usually serves as a good estimate for the mean of the probability distribution of which the points are samples . It is true that if the probability ...
... mean of a distribution . The sample mean or center of gravity of a set of points usually serves as a good estimate for the mean of the probability distribution of which the points are samples . It is true that if the probability ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |