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 128
... elements of A are positive , the next p2 diago- nal elements are negative , and the last d ( p1 + p2 ) diagonal elements are zero , where d is the order of A , and ( p1 + p2 ) is the rank of A. The diagonal elements of A are the ...
... elements of A are positive , the next p2 diago- nal elements are negative , and the last d ( p1 + p2 ) diagonal elements are zero , where d is the order of A , and ( p1 + p2 ) is the rank of A. The diagonal elements of A are the ...
Sisältö
Preface vii | 11 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 important 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 terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |