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 gi ( X ) = p ( Xi ) p ( i ) for i = 1 , ... , R It will often be convenient to use the alternative expression gi ( X ) = log p ( Xi ) + log p ( i ) for i = 1 ...
for this loss function , the discriminant functions can be expressed as gi ( X ) = p ( Xi ) p ( i ) for i = 1 , ... , R It will often be convenient to use the alternative expression gi ( X ) = log p ( Xi ) + log p ( i ) for i = 1 ...
Sivu 52
The expression for the bivariate normal density function for the unnormalized and untranslated variables 1 and 2 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 1 and 2 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 proba- bility distribution is almost identical in form to that of Eq . ( 3.21 ) . It is the following : 1 p ( X ) = ( 2π ) d / 2 exp ( -2 ( X - M ) ' Σ − 1 ( X — M ) } ( 3.24 ) The various ...
The expression for the d - variate normal proba- bility distribution is almost identical in form to that of Eq . ( 3.21 ) . It is the following : 1 p ( X ) = ( 2π ) d / 2 exp ( -2 ( X - M ) ' Σ − 1 ( X — M ) } ( 3.24 ) The various ...
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies 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 classifier pattern hyperplane pattern space pattern vector 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 |
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 |