Magdon-Ismail, Malik (2000) No Free Lunch for Noise Prediction. Neural Computation, 12 (3). pp. 547-564. ISSN 0899-7667. https://resolver.caltech.edu/CaltechAUTHORS:20111128-133815330
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Abstract
No-free-lunch theorems have shown that learning algorithms cannot be universally good. We show that no free funch exists for noise prediction as well. We show that when the noise is additive and the prior over target functions is uniform, a prior on the noise distribution cannot be updated, in the Bayesian sense, from any finite data set. We emphasize the importance of a prior over the target function in order to justify superior performance for learning systems.
| Item Type: | Article | |||||||||
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| Additional Information: | © 2000 Massachusetts Institute of Technology. Received May 29, 1998; accepted March 31, 1999. Posted Online March 13, 2006. We thank Yaser Abu-Mostafa and the members of the Caltech Learning Systems Group for helpful comments. In addition, two anonymous referees provided useful comments. | |||||||||
| Issue or Number: | 3 | |||||||||
| Record Number: | CaltechAUTHORS:20111128-133815330 | |||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20111128-133815330 | |||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | |||||||||
| ID Code: | 27979 | |||||||||
| Collection: | CaltechAUTHORS | |||||||||
| Deposited By: | Tony Diaz | |||||||||
| Deposited On: | 28 Nov 2011 22:18 | |||||||||
| Last Modified: | 03 Oct 2019 03:28 |
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