Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 20
Stated another way , a classification of X is linear and the subsets X1 , X2 , Xr are linearly separable if and only if linear discriminant functions 91 , 92 , exist such that .
Stated another way , a classification of X is linear and the subsets X1 , X2 , Xr are linearly separable if and only if linear discriminant functions 91 , 92 , exist such that .
Sivu 21
Because the decision regions of a linear machine are convex , it is easy to show that if the subsets X1 , X2 , , Xr are linearly separable , then each pair of subsets X1 , X ;, i , j = 1 , R , i j , is also linearly separable .
Because the decision regions of a linear machine are convex , it is easy to show that if the subsets X1 , X2 , , Xr are linearly separable , then each pair of subsets X1 , X ;, i , j = 1 , R , i j , is also linearly separable .
Sivu 107
When these conditions are met , I ( 1 ) and 12 ( 1 ) are guaranteed to be linearly separable , and thus a two - layer ... linear separability Before stating and proving the sufficient condition it will be helpful to make a definition .
When these conditions are met , I ( 1 ) and 12 ( 1 ) are guaranteed to be linearly separable , and thus a two - layer ... linear separability Before stating and proving the sufficient condition it will be helpful to make a definition .
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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 |