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Schlagwörter:
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Zusammenfassung:
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.
