Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 59
Sivu 38
In this case M is 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 ...
In this case M is 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 ...
Sivu 49
values are given by Pi Wi = log [ 2 : ( 1-2 ) ] gi ) i = pi ) Wa + 1 = log p ( 1 ) - p ( 1 ) 1 , 1 · - Pi " Σ [ 1 ] + log ( P. ) = 1 qi d ( 3.14 ) Investigation of Eqs . ( 3.13 ) and ( 3 · 14 ) in some limiting cases will show that the ...
values are given by Pi Wi = log [ 2 : ( 1-2 ) ] gi ) i = pi ) Wa + 1 = log p ( 1 ) - p ( 1 ) 1 , 1 · - Pi " Σ [ 1 ] + log ( P. ) = 1 qi d ( 3.14 ) Investigation of Eqs . ( 3.13 ) and ( 3 · 14 ) in some limiting cases will show that the ...
Sivu 56
The first d weights employed by the ith discriminator are 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 ...
The first d weights employed by the ith discriminator are 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 ...
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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 |
Kirjaluettelon tiedot
| Otsikko | Learning Machines: Foundations of Trainable Pattern-classifying Systems McGraw-Hill series in systems science |
| Kirjoittaja | Nils J. Nilsson |
| Painos | kuvitettu |
| Kustantaja | McGraw-Hill, 1965 |
| Alkuperäisteoksen sijainti | Wisconsinin yliopisto - Madison |
| Digitoitu | 16. huhtikuu 2008 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |