Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 34
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... FIGURE 1.3 Examples of discriminant functions for two - dimensional patterns ... scalar and single - valued functions of the pattern X. These functions , which we call discriminant functions , are chosen such that for all X in Ri , gi ...
... FIGURE 1.3 Examples of discriminant functions for two - dimensional patterns ... scalar and single - valued functions of the pattern X. These functions , which we call discriminant functions , are chosen such that for all X in Ri , gi ...
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... FIGURE 2.2 Examples of decision regions and surfaces resulting from linear discriminant functions 13 R3 R2 OR , 512 $ 23 FIGURE 2.3 Decision regions for a minimum - distance classifier with respect to the points P1 , P2 , and P3 In many ...
... FIGURE 2.2 Examples of decision regions and surfaces resulting from linear discriminant functions 13 R3 R2 OR , 512 $ 23 FIGURE 2.3 Decision regions for a minimum - distance classifier with respect to the points P1 , P2 , and P3 In many ...
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... FIGURE 2.11 An illustration of the construction used in the text for K = 2 , N = 3 , d = 3 We now construct a set of N distinct K - dimensional hyperplanes , each containing Z and one of the points in X. Figure 2.11 illustrates this ...
... FIGURE 2.11 An illustration of the construction used in the text for K = 2 , N = 3 , d = 3 We now construct a set of N distinct K - dimensional hyperplanes , each containing Z and one of the points in X. Figure 2.11 illustrates this ...
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 decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges 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 second layer shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns 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 |