Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 17
Sivu 49
... estimates for the unknown probabili- 1 = * The reader with background in statistics will recall that there are circumstances in which it is possible to make optimum estimates of unknown probability values . These optimum estimates are ...
... estimates for the unknown probabili- 1 = * The reader with background in statistics will recall that there are circumstances in which it is possible to make optimum estimates of unknown probability values . These optimum estimates are ...
Sivu 58
... estimates of M ; and Σi , respectively . The use of these estimates to specify the discriminant functions would constitute a para- metric training method . i An expression that is somewhat simpler than the one in Eq . ( 3 · 36 ) can be ...
... estimates of M ; and Σi , respectively . The use of these estimates to specify the discriminant functions would constitute a para- metric training method . i An expression that is somewhat simpler than the one in Eq . ( 3 · 36 ) can be ...
Sivu 120
... estimate the values of p ( X | i ) p ( i ) for i R around the point X. If these values are approximated by the numbers ... estimates ) . If the training subsets are large , it has been shown that the Fix and Hodges decision rule leads to ...
... estimate the values of p ( X | i ) p ( i ) for i R around the point X. If these values are approximated by the numbers ... estimates ) . If the training subsets are large , it has been shown that the Fix and Hodges decision rule leads to ...
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 |