Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... regions which we shall call decision regions . The ith region R ; is the set of points which map into the ith cate- gory number . For convenience , we shall arbitrarily assume that patterns which lie on decision surfaces do not belong ...
... regions which we shall call decision regions . The ith region R ; is the set of points which map into the ith cate- gory number . For convenience , we shall arbitrarily assume that patterns which lie on decision surfaces do not belong ...
Sivu 19
... regions and surfaces resulting from linear discriminant functions S 13 R3 R2 R1 S 12 S 23 FIGURE 2.3 Decision regions for a minimum - distance classifier with respect to the points P1 , P2 , and P3 In many cases some of the hyperplanes ...
... regions and surfaces resulting from linear discriminant functions S 13 R3 R2 R1 S 12 S 23 FIGURE 2.3 Decision regions for a minimum - distance classifier with respect to the points P1 , P2 , and P3 In many cases some of the hyperplanes ...
Sivu 20
... region lies entirely within the region ) . It will be left as an exercise for the reader to verify that the decision regions of a linear machine are always convex . 2.5 Linear classifications of patterns Suppose we have a finite set X ...
... region lies entirely within the region ) . It will be left as an exercise for the reader to verify that the decision regions of a linear machine are always convex . 2.5 Linear classifications of patterns Suppose we have a finite set X ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector 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 |