Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 47
for this loss function , the discriminant functions can be expressed as g : ( X ) = P ( X ) ( 2 ) for i = 1 , ... , R ( 3.7a ) It will often be convenient to use the alternative expression gi ( X ) = log p ( Xi ) + log p i ) X for i = 1 ...
for this loss function , the discriminant functions can be expressed as g : ( X ) = P ( X ) ( 2 ) for i = 1 , ... , R ( 3.7a ) It will often be convenient to use the alternative expression gi ( X ) = log p ( Xi ) + log p i ) X for i = 1 ...
Sivu 52
The expression for the bivariate normal density function for the unnormalized and untranslated variables x , and x , is more complicated * than that of Eq . ( 3:18 ) , but the general properties of the function are easily described .
The expression for the bivariate normal density function for the unnormalized and untranslated variables x , and x , is more complicated * than that of Eq . ( 3:18 ) , but the general properties of the function are easily described .
Sivu 54
The expression for the d - variate normal probability distribution is almost identical in form to that of Eq . ( 3.21 ) . It is the following : 1 p ( X ) exp { -42 ( X – M ) ^ 2- ( X – M ) } ( 27 ) / 2 29 ( 3.24 ) d 12 The various terms ...
The expression for the d - variate normal probability distribution is almost identical in form to that of Eq . ( 3.21 ) . It is the following : 1 p ( X ) exp { -42 ( X – M ) ^ 2- ( X – M ) } ( 27 ) / 2 29 ( 3.24 ) d 12 The various terms ...
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Sisältö
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative 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 solution space specific Stanford step Suppose theorem theory threshold training methods training patterns 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 |
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 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |