Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 65
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. CHAPTER 4 SOME NONPARAMETRIC TRAINING METHODS FOR MACHINES 4.1 Nonparametric training of a TLU In this chapter we shall introduce some specific nonparametric training ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. CHAPTER 4 SOME NONPARAMETRIC TRAINING METHODS FOR MACHINES 4.1 Nonparametric training of a TLU In this chapter we shall introduce some specific nonparametric training ...
Sivu 119
... nonparametric procedures are being studied for application to " overlapping " pattern subsets . In the next few sections we shall develop some nonparametric procedures for training PWL machines that do not depend on the error ...
... nonparametric procedures are being studied for application to " overlapping " pattern subsets . In the next few sections we shall develop some nonparametric procedures for training PWL machines that do not depend on the error ...
Sivu 121
... nonparametric methods which preserve some of the features of the Fix and Hodges method without requiring the individual storage of every training pattern in a rapid - access memory . We shall discuss one such method in the next section ...
... nonparametric methods which preserve some of the features of the Fix and Hodges method without requiring the individual storage of every training pattern in a rapid - access memory . We shall discuss one such method in the next section ...
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 |