Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 11
... by geometrical representations which can enhance understanding of the concepts underlying these algorithms . Theorems about the convergence properties of the nonparametric training algorithms are stated and proved in Chapter 5.
... by geometrical representations which can enhance understanding of the concepts underlying these algorithms . Theorems about the convergence properties of the nonparametric training algorithms are stated and proved in Chapter 5.
Sivu 109
... ( 1 ) Ci > 0 ( 6:10 ) Ci < 0 > Since Ñ has an inverse ( it has rank equal to P ) , we can always solve for w by CÑ - 1 ( 6 · 11 ) 1 W = Thus , since a threshold and weight vector can always be found , the theorem is proved .
... ( 1 ) Ci > 0 ( 6:10 ) Ci < 0 > Since Ñ has an inverse ( it has rank equal to P ) , we can always solve for w by CÑ - 1 ( 6 · 11 ) 1 W = Thus , since a threshold and weight vector can always be found , the theorem is proved .
Sivu 116
Since the piecewise linear discriminant functions are not o functions , the error - correction training theorems proved in Chapter 5 do not apply to PWL machines . The pattern capacity of PWL machines is also unknown .
Since the piecewise linear discriminant functions are not o functions , the error - correction training theorems proved in Chapter 5 do not apply to PWL machines . The pattern capacity of PWL machines is also unknown .
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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 |