Bilevel model selection for support vector machines
Gautam Kunapuli, Kristin P. Bennett, Jing Fang Hu, Jong‐Shi Pang · CRM proceedings & lecture notes · 2008
Abstract. The successful application of Support Vector Machines (SVMs), kernel methods and other statistical machine learning methods requires selection of model parameters based on estimates of the generalization error. This paper presents a novel approach to systematic model selection through bilevel optimization. We show how modelling tasks for widely used machine learning methods can be formulated as bilevel optimization problems and describe how the approach can address a broad range of tasks—among which are parameter, feature and kernel selection In addition, we also discuss the challenges in implementing these approaches and enumerate opportunities for future work in this emerging research area. 1.