Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 3
... response represents a category into which a pattern may be placed , and we shall accordingly label the response values by the integers 1 , R. It is the purpose of this chapter to develop further this basic model and to introduce two ...
... response represents a category into which a pattern may be placed , and we shall accordingly label the response values by the integers 1 , R. It is the purpose of this chapter to develop further this basic model and to introduce two ...
Sivu 72
... response for one pattern may very well undo a correction made on a previous pattern . Eventually , however , one last correction will be made that does not affect the correct response to the other patterns . 4.4 A numerical example of ...
... response for one pattern may very well undo a correction made on a previous pattern . Eventually , however , one last correction will be made that does not affect the correct response to the other patterns . 4.4 A numerical example of ...
Sivu 98
... response is the majority response of the committee TLUs . A committee machine of size P is depicted in Fig . 6.4 . The committee machine can be generalized by allowing the com- mittee TLUs to have different voting strengths . A further ...
... response is the majority response of the committee TLUs . A committee machine of size P is depicted in Fig . 6.4 . The committee machine can be generalized by allowing the com- mittee TLUs to have different voting strengths . A further ...
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 |