Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 39
Sivu 67
... hyperplane in weight space defined by Eq . ( 4 · 2 ) for a given pattern vector is called the pattern hyperplane . This hyperplane separates the space of weight points into two classes : Those which for the pattern Y produce a TLU ...
... hyperplane in weight space defined by Eq . ( 4 · 2 ) for a given pattern vector is called the pattern hyperplane . This hyperplane separates the space of weight points into two classes : Those which for the pattern Y produce a TLU ...
Sivu 69
... pattern hyperplane . The most direct path to the other side is along a line normal to the pattern hyperplane . Such a motion can be achieved by adding the augmented pattern vector Y to W to create a new weight vector W ' . Each TLU ...
... pattern hyperplane . The most direct path to the other side is along a line normal to the pattern hyperplane . Such a motion can be achieved by adding the augmented pattern vector Y to W to create a new weight vector W ' . Each TLU ...
Sivu 71
... pattern hyperplane is always the same . This fixed distance may or may not be sufficient to cross the pattern hyperplane and thus correct the error . In another case , c is chosen to be just large enough to Initial weight point Final ...
... pattern hyperplane is always the same . This fixed distance may or may not be sufficient to cross the pattern hyperplane and thus correct the error . In another case , c is chosen to be just large enough to Initial weight point Final ...
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 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 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 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 |