| dc.contributor.author | Mukherjee, Sayan | en_US |
| dc.contributor.author | Niyogi, Partha | en_US |
| dc.contributor.author | Poggio, Tomaso | en_US |
| dc.contributor.author | Rifkin, Ryan | en_US |
| dc.date.accessioned | 2004-08-31T18:12:01Z | |
| dc.date.available | 2004-08-31T18:12:01Z | |
| dc.date.issued | 2002-12-01 | en_US |
| dc.identifier.other | AIM-2002-024 | en_US |
| dc.identifier.other | CBCL-223 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1721.3/5507 | |
| dc.description | revised July 2003 | en_US |
| dc.description.abstract | Solutions of learning problems by Empirical Risk Minimization (ERM) need to be consistent, so that they may be predictive. They also need to be well-posed, so that they can be used robustly. We show that a statistical form of well-posedness, defined in terms of the key property of L-stability, is necessary and sufficient for consistency of ERM. | en_US |
| dc.format.extent | 24 p. | en_US |
| dc.format.extent | 1854466 bytes | |
| dc.format.extent | 400508 bytes | |
| dc.format.mimetype | application/postscript | |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en_US | |
| dc.relation.ispartofseries | AIM-2002-024 | en_US |
| dc.relation.ispartofseries | CBCL-223 | en_US |
| dc.subject | AI | en_US |
| dc.subject | Theory of Learning | en_US |
| dc.subject | Great Discoveries | en_US |
| dc.subject | Consistency | en_US |
| dc.subject | ERM | en_US |
| dc.subject | Stability | en_US |
| dc.title | Statistical Learning: Stability is Sufficient for Generalization and Necessary and Sufficient for Consistency of Empirical Risk Minimization | en_US |
