Name
Abstract | ||
|---|---|---|
We define a generalized likelihood function based on uncertainty measures and show that maximizing such a likelihood function for different measures induces different types of classifiers. In the probabilistic framework, we obtain classifiers that optimize the cross-entropy function. In the possibilistic framework, we obtain classifiers that maximize the interclass margin. Furthermore, we show that the support vector machine is a sub-class of these maximummargin classifiers. |
Year | Venue | Keywords |
|---|---|---|
2013 | UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence | uncertainty framework,support vector machine,probabilistic framework,different measure,cross-entropy function,likelihood function,interclass margin,generalized likelihood,maximummargin classifier,possibilistic framework,different type,cross entropy |
DocType | Volume | ISBN |
Journal | abs/1301.3896 | 1-55860-709-9 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Loo-Nin Teow | 1 | 103 | 17.29 |
| Kia-Fock Loe | 2 | 180 | 20.88 |