Mathematical programming for multiple kernellearning

Zien, A

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Mathematical programming for multiple kernellearning

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Zien, A
Friedrich Miescher Laboratory, Max Planck Society;

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https://www.euro-online.org/media_site/reports/EURO23_AB.pdf
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Citation

Zien, A. (2009). Mathematical programming for multiple kernellearning. In 23rd European Conference on Operational Research (Euro XXIII) (pp. 187).

Cite as: https://hdl.handle.net/21.11116/0000-0003-28D4-1

Abstract

Many machine learning problems (e.g. training SVMs) have a
mathematical programming (MP) formulation. An important advantage
of SVMs is elegant non-linear modelling via kernel functions;
however, a proper choice of kernel is crucial for accurate predictions.
Multiple kernel learning (MKL) is an extension to SVMs
that allows to optimize a linear combination of kernels during training.
We review MKL, present its different MP formulations, and
investigate their time complexity. Finally we discuss what is key
for tuning mathematical programming to achieve the required computational
efficiency.