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 , XR are linearly separable if and only if linear discriminant functions 91,92 , ... , GR exist such that X2 , g : ( X ) > gi ( X ) j = 1 , R , i ti for all X in Xi ...
Stated another way , a classification of X is linear and the subsets X1 , XR are linearly separable if and only if linear discriminant functions 91,92 , ... , GR exist such that X2 , g : ( X ) > gi ( X ) j = 1 , R , i ti for all X in Xi ...
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 Xi , X ;, i , j = 1 , ... R , i * j , is also linearly ...
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 Xi , X ;, i , j = 1 , ... R , i * j , is also linearly ...
Sivu 107
When these conditions are met , I , ( 1 ) and I2 ( 1 ) are guaranteed to be linearly separable , and thus a two - layer ... 6 : 6 A sufficient condition for image - space linear separability Before stating and proving the sufficient ...
When these conditions are met , I , ( 1 ) and I2 ( 1 ) are guaranteed to be linearly separable , and thus a two - layer ... 6 : 6 A sufficient condition for image - space linear separability Before stating and proving the sufficient ...
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Sisältö
Preface vii | 11 |
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 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 known layered machine linear dichotomies linear machine linearly separable measurements negative 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 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 |