Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 34
Sivu 19
... FIGURE 2.2 Examples of decision regions and surfaces resulting from linear discriminant functions S 13 R3 R2 R1 12 S 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 S 13 R3 R2 R1 12 S 23 FIGURE 2.3 Decision regions for a minimum - distance classifier with respect to the points P1 , P2 , and P3 In many ...
Sivu 68
... FIGURE 4.1 A two - dimensional weight space with three pattern hyperplanes encircled numbers attached to the hyperplanes indicate the number of the pattern . Thus , the solution region and the solution weight point W indicated in the figure ...
... FIGURE 4.1 A two - dimensional weight space with three pattern hyperplanes encircled numbers attached to the hyperplanes indicate the number of the pattern . Thus , the solution region and the solution weight point W indicated in the figure ...
Sivu 96
... FIGURE 6.1 A network of TLUS TLUS in the preceding layer only . The output of the single TLU in the final layer is the response of the machine . Layered machines can implement quite complex decision surfaces . We shall see later in this ...
... FIGURE 6.1 A network of TLUS TLUS in the preceding layer only . The output of the single TLU in the final layer is the response of the machine . Layered machines can implement quite complex decision surfaces . We shall see later in 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 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 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 |