Name
Title | ||
|---|---|---|
Towards an automated method based on Iterated Local Search optimization for tuning the parameters of Support Vector Machines. |
Abstract | ||
|---|---|---|
We provide preliminary details and formulation of an optimization strategy under current development that is able to automatically tune the parameters of a Support Vector Machine over new datasets. The optimization strategy is a heuristic based on Iterated Local Search, a modification of classic hill climbing which iterates calls to a local search routine. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Artificial Intelligence | Hill climbing,Heuristic,Computer science,Support vector machine,Artificial intelligence,Local search (optimization),Iterated function,Iterated local search,Machine learning |
DocType | Volume | Citations |
Journal | abs/1707.03191 | 0 |
PageRank | References | Authors |
0.34 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sergio Consoli | 1 | 164 | 24.96 |
| Jacek Kustra | 2 | 36 | 3.59 |
| Pieter C. Vos | 3 | 0 | 0.34 |
| Monique Hendriks | 4 | 0 | 0.68 |
| Dimitrios Mavroeidis | 5 | 130 | 9.50 |