Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. i the decision surfaces divide Ed into R regions which we shall call decision regions . The ith region R , is the set of points which map into the ith cate- gory ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. i the decision surfaces divide Ed into R regions which we shall call decision regions . The ith region R , is the set of points which map into the ith cate- gory ...
Sivu 18
... decision surfaces of linear machines Suppose that two decision regions R , and R ; of a linear machine share a common boundary . The decision surface separating these two regions is then a segment of the surface Si ; having the equation ...
... decision surfaces of linear machines Suppose that two decision regions R , and R ; of a linear machine share a common boundary . The decision surface separating these two regions is then a segment of the surface Si ; having the equation ...
Sivu 28
... decision surfaces The decision surfaces of quadric machines are sections of second - degree surfaces which we shall call quadric surfaces . Specifically , if R , and R , share a common boundary , it is a section of the surface S1 ...
... decision surfaces The decision surfaces of quadric machines are sections of second - degree surfaces which we shall call quadric surfaces . Specifically , if R , and R , share a common boundary , it is a section of the surface S1 ...
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 |