Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 59
Sivu 38
... given by · " Пр = " 1Xk22 n1 n2 T M = - Σ ( α + 1 - 1 ) d i i ― = ( a + " ) - 1 If r 2 we have a general quadric function for which M = as derived in Sec . 2.10 . For general quadric functions Ž ( N , d ) = LN , d ( d + 3 ) 2 ( 2.42 ) d ...
... given by · " Пр = " 1Xk22 n1 n2 T M = - Σ ( α + 1 - 1 ) d i i ― = ( a + " ) - 1 If r 2 we have a general quadric function for which M = as derived in Sec . 2.10 . For general quadric functions Ž ( N , d ) = LN , d ( d + 3 ) 2 ( 2.42 ) d ...
Sivu 49
... given by ( n + 1 ) / ( N + 2 ) . Comparing this expression with those of Eq . ( 3.15 ) might lead the reader to assume that the estimates for the Pi and qi could be improved by a slight modification . However , in view of the fact that ...
... given by ( n + 1 ) / ( N + 2 ) . Comparing this expression with those of Eq . ( 3.15 ) might lead the reader to assume that the estimates for the Pi and qi could be improved by a slight modification . However , in view of the fact that ...
Sivu 56
... given by the values of the components of the transformed mean vector , Σ - 1M ;; the ( d + 1 ) th weight is given by the value of the constant , = If R 2 , and if Σ1 g ( X ) can be written as g ( X ) = ΧΣ - ' ( Μι - = log pi1⁄2M , ' Σ ...
... given by the values of the components of the transformed mean vector , Σ - 1M ;; the ( d + 1 ) th weight is given by the value of the constant , = If R 2 , and if Σ1 g ( X ) can be written as g ( X ) = ΧΣ - ' ( Μι - = log pi1⁄2M , ' Σ ...
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 step subsidiary discriminant Suppose terns 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,Lįszló Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |