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 two - category pattern ...
... 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 two - category pattern ...
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 ...
... 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 ...
Sivu 48
... element w d Summing device X : Pattern +1 10 d + 1 Weights FIGURE 3.1 The optimum classifier for binary patterns whose components are statistically independent linear in this case . Therefore a TLU can be used as the optimum classifying ...
... element w d Summing device X : Pattern +1 10 d + 1 Weights FIGURE 3.1 The optimum classifier for binary patterns whose components are statistically independent linear in this case . Therefore a TLU can be used as the optimum classifying ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |