Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 27
Sivu 72
... correction rule is convergent . By convergent we mean that when the pattern training subsets are linearly separable , the sequence of TLU weight vectors produced by the training procedure con- verges toward a solution weight vector ...
... correction rule is convergent . By convergent we mean that when the pattern training subsets are linearly separable , the sequence of TLU weight vectors produced by the training procedure con- verges toward a solution weight vector ...
Sivu 119
... correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision surface , an error - correction procedure could never stabilize at the optimum surface since inevitable errors would ...
... correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision surface , an error - correction procedure could never stabilize at the optimum surface since inevitable errors would ...
Sivu 133
... correction train- ing methods , 71 , 72 , 75 of fixed - increment rule , 82 , 85 of fractional correction rule , 91 of generalized error - correction rule , 89 , 90 Convergence theorem , perceptron , 79 Convexity of decision regions ...
... correction train- ing methods , 71 , 72 , 75 of fixed - increment rule , 82 , 85 of fractional correction rule , 91 of generalized error - correction rule , 89 , 90 Convergence theorem , perceptron , 79 Convexity of decision regions ...
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 decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges 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 |