Towards an automated method based on Iterated Local Search optimization for tuning the parameters of Support Vector Machines
arXiv:1707.03191v11 citations
Originality Synthesis-oriented
AI Analysis
This is an incremental approach for researchers or practitioners needing automated parameter tuning in machine learning.
The paper tackles the problem of automatically tuning Support Vector Machine parameters for new datasets by proposing a heuristic optimization strategy based on Iterated Local Search, but no concrete results or numbers are provided as it is still under development.
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.