Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 24
Sivu 72
... rule ; we shall do so for a set of three - dimensional pat- terns with binary components using the fixed - increment correction rule with c = 1. The training set of augmented pattern vectors and their de- sired responses is shown below ...
... rule ; we shall do so for a set of three - dimensional pat- terns with binary components using the fixed - increment correction rule with c = 1. The training set of augmented pattern vectors and their de- sired responses is shown below ...
Sivu 101
... rule is used to adjust 1⁄2 ( | N | + 1 ) of the ( P + Nk ) / 2 weight vectors making nonnegative dot products with Y. Those 11⁄2 ( N + 1 ) having the least - positive ( but not negative ) dot prod- ucts are adjusted by the rule W , ( k ...
... rule is used to adjust 1⁄2 ( | N | + 1 ) of the ( P + Nk ) / 2 weight vectors making nonnegative dot products with Y. Those 11⁄2 ( N + 1 ) having the least - positive ( but not negative ) dot prod- ucts are adjusted by the rule W , ( k ...
Sivu 133
... rule , 82 , 85 of fractional correction rule , 91 of generalized error - correction rule , 89 , 90 Convergence theorem , perceptron , 79 Convexity of decision regions , 20 Cooper , 62 , 63 Correction increment , 69 , 75 , 80 , 101 ...
... rule , 82 , 85 of fractional correction rule , 91 of generalized error - correction rule , 89 , 90 Convergence theorem , perceptron , 79 Convexity of decision regions , 20 Cooper , 62 , 63 Correction increment , 69 , 75 , 80 , 101 ...
Sisältö
Preface vii | 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 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 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 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 |