Name
Abstract | ||
|---|---|---|
A number of learning tasks can be solved robustly using key concepts from statistical learning theory. In this paper we first summarize the main concepts of statistical learning theory, a framework in which certain learning from examples problems, namely classification, regression, and density estimation, have been studied in a principled way. We then show how the key concepts of the theory can be used not only for these standard learning from examples problems, but also for many others. In particular we discuss how to learn functions which model a preference relation. The goal is to illustrate the value of statistical learning theory beyond the standard framework it has been used until now. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/3-540-45808-5_2 | WIRN |
Keywords | Field | DocType |
key concept,preference relation,density estimation,standard framework,standard learning,certain learning,learning preference relations,examples problem,statistical learning theory,main concept | Statistical learning theory,Algorithmic learning theory,Instance-based learning,Stability (learning theory),Pattern recognition,Computer science,Unsupervised learning,Preference learning,Artificial intelligence,Computational learning theory,Machine learning,Sample exclusion dimension | Conference |
Volume | ISSN | ISBN |
2486 | 0302-9743 | 3-540-44265-0 |
Citations | PageRank | References |
2 | 0.36 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Theodoros Evgeniou | 1 | 3005 | 219.65 |
| Massimiliano Pontil | 2 | 5820 | 472.96 |