An Empirical Evaluation of Simple Performance Measures for Tuning SVM Hyperparameters

摘要：

Choosing optimal hyperparameters for support vector machines is an important step in SVM design. This is usually done by minimizing either an estimate of generalization error or some other related performance measure. In this paper, we empirically study the usefulness of several simple performance measures that are very inexpensive to compute. The results clearly point out which of these performance measures are adequate functionals for tuning SVM hyperparameters. For SVMs with LI soft-margin formulation, none of the simple measures yields a performance as good as k-fold cross-validation.

作者: Kaibo Duan S Sathiya Keerthi Aun Neow Poo

作者单位: Department of Mechanical Engineering National University of Singapore Singapore 119260

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 893-898

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）