Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... element whose threshold value is equal to zero . For this reason the threshold element assumes an important role in pattern - classifying machines . We shall use the block diagram of Fig . 1 · 5 as a basic model of a two - category ...
... element whose threshold value is equal to zero . For this reason the threshold element assumes an important role in pattern - classifying machines . We shall use the block diagram of Fig . 1 · 5 as a basic model of a two - category ...
Sivu 21
... element , is called a threshold logic unit ( TLU ) . We shall ordinarily assume that the threshold element is a device which responds with a +1 signal if g ( X ) > 0 and a -1 signal if g ( X ) < 0. We must then associate a TLU output of ...
... element , is called a threshold logic unit ( TLU ) . We shall ordinarily assume that the threshold element is a device which responds with a +1 signal if g ( X ) > 0 and a -1 signal if g ( X ) < 0. We must then associate a TLU output of ...
Sivu 108
... elements equal to 0 and 1 . Because the partition is nonredundant , if we remove any row from M , at least two columns of the reduced matrix will be identical . ( Re- moving a row corresponds to removing a TLU and thus collapsing two ...
... elements equal to 0 and 1 . Because the partition is nonredundant , if we remove any row from M , at least two columns of the reduced matrix will be identical . ( Re- moving a row corresponds to removing a TLU and thus collapsing two ...
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 |