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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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |