Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 44
Sivu 3
... classifier a device with d input lines and one output line ( see Fig . 1.1 ) . The d input lines are activated simultaneously by the pattern , and the output line responds * x1 : xa Pattern ( data to be ... PATTERN CLASSIFIERS 3.
... classifier a device with d input lines and one output line ( see Fig . 1.1 ) . The d input lines are activated simultaneously by the pattern , and the output line responds * x1 : xa Pattern ( data to be ... PATTERN CLASSIFIERS 3.
Sivu 7
... classifier method will produce a more detailed functional block diagram of the basic model for a pattern classifier discussed in Sec . 1.2 . Our discriminant- function pattern classifier , illustrated in Fig ... PATTERN CLASSIFIERS 7.
... classifier method will produce a more detailed functional block diagram of the basic model for a pattern classifier discussed in Sec . 1.2 . Our discriminant- function pattern classifier , illustrated in Fig ... PATTERN CLASSIFIERS 7.
Sivu 9
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. We shall assume here that little if any a ... classifier is eventually to achieve must be achieved largely by an adjustment process , which has become known as ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. We shall assume here that little if any a ... classifier is eventually to achieve must be achieved largely by an adjustment process , which has become known as ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS correction increment covariance matrix d-dimensional 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 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 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 |