Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 14
Sivu 6
Let gı ( X ) , 92 ( X ) , . . . , 9R ( X ) be R , FIGURE 1 . 3 Examples of discriminant
functions for two - dimensional patterns scalar and single - valued functions of the
pattern X . These functions , which we call discriminant functions , are chosen ...
Let gı ( X ) , 92 ( X ) , . . . , 9R ( X ) be R , FIGURE 1 . 3 Examples of discriminant
functions for two - dimensional patterns scalar and single - valued functions of the
pattern X . These functions , which we call discriminant functions , are chosen ...
Sivu 24
Let us define the Euclidean distance d ( X , Pi ) from an arbitrary point X to the
point set Pi by d ( X , P : ) = min j = 1 , . . . ... For each i = 1 , . . . , R , we define the
functions gi ( X ) = max { P : 6 ) . X – 12P : 6 ) . P ; ( ) ) } ( 2 : 17 ) j = 1 , . . . , Li Note
...
Let us define the Euclidean distance d ( X , Pi ) from an arbitrary point X to the
point set Pi by d ( X , P : ) = min j = 1 , . . . ... For each i = 1 , . . . , R , we define the
functions gi ( X ) = max { P : 6 ) . X – 12P : 6 ) . P ; ( ) ) } ( 2 : 17 ) j = 1 , . . . , Li Note
...
Sivu 55
4 we learned that for a symmetric loss function , the optimum classifier uses the
discriminant functions given by gi ( X ) = log p ( X \ 2 ) + log pi į = 1 , . . . , R ( 3 . 29
) Using Eq . ( 3 . 28 ) , the gi ( X ) are given as follows : DI 9 : ( X ) = log Pi – log 27
...
4 we learned that for a symmetric loss function , the optimum classifier uses the
discriminant functions given by gi ( X ) = log p ( X \ 2 ) + log pi į = 1 , . . . , R ( 3 . 29
) Using Eq . ( 3 . 28 ) , the gi ( X ) are given as follows : DI 9 : ( X ) = log Pi – log 27
...
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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 Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements networks 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 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 |