Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 41
Sivu 99
... training procedure for committee machines Suppose that we have training pattern subsets Y1 and Y2 , comprising the training set Y , and we wish to find a committee machine of size P to separate these subsets . To accomplish this , we ...
... training procedure for committee machines Suppose that we have training pattern subsets Y1 and Y2 , comprising the training set Y , and we wish to find a committee machine of size P to separate these subsets . To accomplish this , we ...
Sivu 117
... training problem since it might be unknown beforehand how many subsidiary discriminators should be in each bank . Thus , training ... procedure would involve forming a training se- quence of patterns and presenting these patterns , one at a ...
... training problem since it might be unknown beforehand how many subsidiary discriminators should be in each bank . Thus , training ... procedure would involve forming a training se- quence of patterns and presenting these patterns , one at a ...
Sivu 119
... training methods . Suppose that we decided to use an error - 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 ...
... training methods . Suppose that we decided to use an error - 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 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |