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• W2608221418 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." @default.
• W2608221418 created "2017-05-05" @default.
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• W2608221418 date "2008-04-09" @default.
• W2608221418 modified "2023-09-25" @default.
• W2608221418 title "Bilevel model selection for support vector machines" @default.
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• W2608221418 doi "https://doi.org/10.1090/crmp/045/07" @default.
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