Fast model selection by limiting SVM training times
This addresses the bottleneck of slow model selection for SVM users, though it is an incremental improvement over existing tuning methods.
The paper tackles the problem of time-consuming parameter tuning for kernelized Support Vector Machines (SVMs) by proposing a stopping criterion that limits training time during tuning, reducing model selection times by an order of magnitude.
Kernelized Support Vector Machines (SVMs) are among the best performing supervised learning methods. But for optimal predictive performance, time-consuming parameter tuning is crucial, which impedes application. To tackle this problem, the classic model selection procedure based on grid-search and cross-validation was refined, e.g. by data subsampling and direct search heuristics. Here we focus on a different aspect, the stopping criterion for SVM training. We show that by limiting the training time given to the SVM solver during parameter tuning we can reduce model selection times by an order of magnitude.