Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 90
Sivu 9
... training set . The desired classifications of these patterns are assumed to be known . Discriminant functions are then chosen , by methods to be discussed in general below and more specifically later , which perform adequately on the ...
... training set . The desired classifications of these patterns are assumed to be known . Discriminant functions are then chosen , by methods to be discussed in general below and more specifically later , which perform adequately on the ...
Sivu 11
... training methods . The mathematical foundation underlying these training meth- ods seems to be more extensive than the theory supporting the nonpara- metric training methods . On the other hand , employment of decision- theoretic methods ...
... training methods . The mathematical foundation underlying these training meth- ods seems to be more extensive than the theory supporting the nonpara- metric training methods . On the other hand , employment of decision- theoretic methods ...
Sivu 122
... training set . In the next section we shall present a candidate training method . 7.6 Mode - seeking and related training methods for PWL machines To apply the closest - mode decision method , we need a training procedure to locate the ...
... training set . In the next section we shall present a candidate training method . 7.6 Mode - seeking and related training methods for PWL machines To apply the closest - mode decision method , we need a training procedure to locate the ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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assume augmented pattern belonging to category binary Chapter cluster committee machine committee TLUS correction increment corresponding covariance matrix decision surfaces denote discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(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 partition pattern classifier pattern hyperplane pattern space pattern vector 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 TLU response 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 |