Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 37
Sivu 28
... called positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic form will never be negative , and it and A are called positive semidefinite . 2.9 Quadric decision ...
... called positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic form will never be negative , and it and A are called positive semidefinite . 2.9 Quadric decision ...
Sivu 67
... called the pattern hyperplane . This hyperplane separates the space of weight points into two classes : Those which for the pattern Y produce a TLU response of one are on one side of the hyperplane , called the positive side , and those ...
... called the pattern hyperplane . This hyperplane separates the space of weight points into two classes : Those which for the pattern Y produce a TLU response of one are on one side of the hyperplane , called the positive side , and those ...
Sivu 69
... called the correction increment . It controls the extent of the adjustment . For sufficiently large c , the weight point will cross the pattern hyperplane , and Y. W ' will be correctly positive . If W were incorrectly on the positive ...
... called the correction increment . It controls the extent of the adjustment . For sufficiently large c , the weight point will cross the pattern hyperplane , and Y. W ' will be correctly positive . If W were incorrectly on the positive ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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assume belonging to category cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(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 parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying patterns belonging perceptron piecewise linear 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 TLU response 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 |