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Bilevel model selection for support vector machines. (English) Zbl 1145.68488
Pardalos, Panos M. (ed.) et al., Data mining and mathematical programming. Chapters of the book are based on lectures at the workshop, Montreal, Canada, October 10–13, 2006. Providence, RI: American Mathematical Society (AMS) (ISBN 978-0-8218-4352-9/pbk). CRM Proceedings and Lecture Notes 45, 129-158 (2008).
Summary: The successful application of support vector machines 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.
For the entire collection see [Zbl 1137.68011].

MSC:
68T05 Learning and adaptive systems in artificial intelligence
Software:
filterSQP; NEOS
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