Name
Abstract | ||
|---|---|---|
In Inductive Logic Programming (ILP), algorithms which are purely of the bottom-up or top-down type encounter several problems in practice. Since a majority of them axe greedy ones, these algorithms find clauses in local optima, according to the "quality" measure used for evaluating the results. Moreover, when learning clauses one by one, induced clauses become less interesting to cover few remaining examples. In this paper, we propose a simulated annealing framework to overcome these problems. Using a refinement operator, we define neighborhood relations on clauses and on hypotheses (i.e. sets of clauses). With these relations and appropriate quality measures, we show how to induce clauses (in a coverage approach), or to induce hypotheses directly by using simulated annealing algorithms. We discuss the necessary conditions on the refinement operators and the evaluation measures in order to increase the algorithm's effectivity. Implementations are described and experimentation results are presented. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1007/978-3-540-30109-7_22 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
simulated annealing,simulated annealing algorithm,bottom up,top down | Inductive logic programming,Simulated annealing,Mathematical optimization,Computer science,Local optimum,Greedy algorithm,Implementation,Theoretical computer science,Operator (computer programming),Local search (optimization) | Conference |
Volume | ISSN | Citations |
3194 | 0302-9743 | 9 |
PageRank | References | Authors |
0.78 | 15 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mathieu Serrurier | 1 | 267 | 26.94 |
| Henri Prade | 2 | 10549 | 1445.02 |
| Gilles Richard | 3 | 47 | 4.50 |