Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 23
Sivu 20
... XR are linearly separable if and only if linear discriminant functions 91,92 , ... , GR exist such that X2 , g : ( X ) ... and X2 is a linear dichotomy if and only if a linear discriminant function g exists such that g ( x ) > 0 g ( x ) ...
... XR are linearly separable if and only if linear discriminant functions 91,92 , ... , GR exist such that X2 , g : ( X ) ... and X2 is a linear dichotomy if and only if a linear discriminant function g exists such that g ( x ) > 0 g ( x ) ...
Sivu 75
... procedure can be used to train the general linear machine and thus find ( solution ) weight vectors when they exist . ... to find a set of solution weight vectors after only a finite number of adjustments if such a set exists .
... procedure can be used to train the general linear machine and thus find ( solution ) weight vectors when they exist . ... to find a set of solution weight vectors after only a finite number of adjustments if such a set exists .
Sivu 97
There do not yet exist , however , efficient adjustment rules for such thorough training of a layered machine . ... That is , no vector W exists such that Y.W > 0 for each Y in Yi and ( 6.1 ) Y.W < 0 for each Y in Y2 Therefore ...
There do not yet exist , however , efficient adjustment rules for such thorough training of a layered machine . ... That is , no vector W exists such that Y.W > 0 for each Y in Yi and ( 6.1 ) Y.W < 0 for each Y in Y2 Therefore ...
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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 |