Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 54
Sivu 5
... point and the pattern vector by the symbol X. A pattern classifier is thus a device which maps the points of Ed into the category numbers , 1 , . R. Let the symbol R ; denote the set of " x2 R3 3 . 2 R , R The point ( 5 , -3 ) FIGURE 1.2 ...
... point and the pattern vector by the symbol X. A pattern classifier is thus a device which maps the points of Ed into the category numbers , 1 , . R. Let the symbol R ; denote the set of " x2 R3 3 . 2 R , R The point ( 5 , -3 ) FIGURE 1.2 ...
Sivu 32
... points satisfy some quite mild conditions , the number of dichotomies that can be implemented by a ☀ function will depend only on the number of patterns N and the number of parameters M + 1 of the ☀ function , not on the configuration ...
... points satisfy some quite mild conditions , the number of dichotomies that can be implemented by a ☀ function will depend only on the number of patterns N and the number of parameters M + 1 of the ☀ function , not on the configuration ...
Sivu 36
... points and a set Z of K points ( K < d ) in Ea . We desire to know the number Lz ( N , d ) of linear dichot- omies of X achievable by a hyperplane constrained to contain all the points of Z. We shall assume that the points of Z are in ...
... points and a set Z of K points ( K < d ) in Ea . We desire to know the number Lz ( N , d ) of linear dichot- omies of X achievable by a hyperplane constrained to contain all the points of Z. We shall assume that the points of Z are in ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |