Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 27
Sivu 70
... fixed - increment rule , c is taken to be any fixed number greater than zero . When c is equal to one , for example , each weight is altered by the addition ( or subtraction ) of the corresponding pattern component . This adjustment may ...
... fixed - increment rule , c is taken to be any fixed number greater than zero . When c is equal to one , for example , each weight is altered by the addition ( or subtraction ) of the corresponding pattern component . This adjustment may ...
Sivu 71
... fixed constant so that the distance moved toward a particular pattern hyperplane is always the same . This fixed distance may or may not be sufficient to cross the pattern hyperplane and thus correct the error . In another case , c is ...
... fixed constant so that the distance moved toward a particular pattern hyperplane is always the same . This fixed distance may or may not be sufficient to cross the pattern hyperplane and thus correct the error . In another case , c is ...
Sivu 107
... fixed image - space hyperplane . If the image - space hyperplane is not fixed , then we need only find a transformation which leaves 91 ( 1 ) and 92 ( 1 ) linearly separable . For any given training subsets X1 and X2 it would be of ...
... fixed image - space hyperplane . If the image - space hyperplane is not fixed , then we need only find a transformation which leaves 91 ( 1 ) and 92 ( 1 ) linearly separable . For any given training subsets X1 and X2 it would be of ...
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 |