Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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The next step in the proof is to form from the reduced training sequence Sy and the reduced weight - vector sequences SW , . SW a corresponding sequence of vectors from the set z . Let us denote this sequence of vectors from Z by the ...
The next step in the proof is to form from the reduced training sequence Sy and the reduced weight - vector sequences SW , . SW a corresponding sequence of vectors from the set z . Let us denote this sequence of vectors from Z by the ...
Sivu 90
We apply this rule to each element of Sý to generate the sequence Sz . The final step of the proof is to form a sequence Sy of RD - dimensional weight vectors from the reduced weight - vector sequences , SW , Let Vk be the kth member of ...
We apply this rule to each element of Sý to generate the sequence Sz . The final step of the proof is to form a sequence Sy of RD - dimensional weight vectors from the reduced weight - vector sequences , SW , Let Vk be the kth member of ...
Sivu 136
... category training theorem , 87 rth - order polynomial functions , 30 , 38 Randall , 62 , 63 Rank of sample covariance matrix , 58 Rao , 12 , 13 Reduced training sequence , 82 Reduced weight - vector sequence , 82 Reduction of number.
... category training theorem , 87 rth - order polynomial functions , 30 , 38 Randall , 62 , 63 Rank of sample covariance matrix , 58 Rao , 12 , 13 Reduced training sequence , 82 Reduced weight - vector sequence , 82 Reduction of number.
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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 |