Name
Abstract | ||
|---|---|---|
In the context of knowledge bases expressed in Description Logics, a method for learning functions that can predict the ranking of resources encoding some preference criteria implicitly encoded through examples of rated individuals. The method relies on a kernelized version of the PERCEPTRON RANKING algorithm which is suitable for batch but also online problem settings. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.3233/978-1-60750-606-5-985 | ECAI |
Keywords | Field | DocType |
perceptron ranking algorithm,online problem setting,knowledge base,kernelized version,towards learning,preference criterion,description logics,learning to rank,description logic | Learning to rank,Ranking SVM,Ranking,Computer science,Description logic,Artificial intelligence,Perceptron,Machine learning,Encoding (memory) | Conference |
Volume | ISSN | Citations |
215 | 0922-6389 | 3 |
PageRank | References | Authors |
0.40 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicola Fanizzi | 1 | 1124 | 90.54 |
| Claudia D'Amato | 2 | 733 | 57.03 |
| Floriana Esposito | 3 | 2434 | 277.96 |