Name
Abstract | ||
|---|---|---|
In this paper, we are interested in learning stratified hypotheses from examples and counter-examples associated with weights that express their prototypical importance. It leads to an extension of the well-known version space learning framework. In order to do that, we emphasize that the treatment of positive and negative examples in version space learning is reminding of a bipolar revision process recently studied in the setting of possibilistic information representation. Bipolarity appears when the positive and negative sides of information are specified in a distinct way. Then, we use the possibilistic bipolar representation setting, which distinguishes between what is guaranteed to be possible, and what is simply not impossible, as a basis for extending version space learning to examples associated with possibility degrees. It allows us to define a formal framework for learning layered hypotheses. |
Year | Venue | Keywords |
|---|---|---|
2006 | ECAI | possibilistic information representation,well-known version space,version space,possibilistic hypothesis,possibilistic bipolar representation setting,negative side,negative example,version space learning,layered hypothesis,formal framework,bipolar revision process |
Field | DocType | Volume |
Computer science,Artificial intelligence,Machine learning,Information representation,Version space | Conference | 141 |
ISSN | ISBN | Citations |
0922-6389 | 1-58603-642-4 | 3 |
PageRank | References | Authors |
0.74 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Henri Prade | 1 | 10549 | 1445.02 |
| Mathieu Serrurier | 2 | 267 | 26.94 |