Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 10
Sivu 80
... sequence of weight vectors Sw such that , for some index ko , Wko + 1 = = Wko + 2 =・・・ satisfies inequalities ( 5.1 ) . The initial weight vector W1 is arbitrary . We shall be interested here in sequences Sw , which are recursively ...
... sequence of weight vectors Sw such that , for some index ko , Wko + 1 = = Wko + 2 =・・・ satisfies inequalities ( 5.1 ) . The initial weight vector W1 is arbitrary . We shall be interested here in sequences Sw , which are recursively ...
Sivu 82
... sequence Sw can now be produced recursively from the training sequence Sy by the simplified rule Wx + 1 = Wk Wk + 1 W + Yx ' = • if Yk ' Wk > 0 if Yk ' . Wk < 0 ( 5.8 ) where c is assumed equal to unity . Each time the weight vector is ...
... sequence Sw can now be produced recursively from the training sequence Sy by the simplified rule Wx + 1 = Wk Wk + 1 W + Yx ' = • if Yk ' Wk > 0 if Yk ' . Wk < 0 ( 5.8 ) where c is assumed equal to unity . Each time the weight vector is ...
Sivu 91
... sequence on y ' . The fractional correction rule generates a weight - vector sequence Sw as follows : Begin with any arbitrary weight vector W1 . Then , for k = 1 , 2 , · · 9 Wk + 1 = Wk + λ Wk + 1 = Wk WxYk Yk ' • Yk ' Y ' if YW < 0 ...
... sequence on y ' . The fractional correction rule generates a weight - vector sequence Sw as follows : Begin with any arbitrary weight vector W1 . Then , for k = 1 , 2 , · · 9 Wk + 1 = Wk + λ Wk + 1 = Wk WxYk Yk ' • Yk ' Y ' if YW < 0 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
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 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 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 |