An Extended Level Method for Efficient Multiple Kernel Learning

NeurIPS 2008 Zenglin XuRong JinIrwin KingMichael Lyu

We consider the problem of multiple kernel learning (MKL), which can be formulated as a convex-concave problem. In the past, two efficient methods, i.e., Semi-Infinite Linear Programming (SILP) and Subgradient Descent (SD), have been proposed for large-scale multiple kernel learning... (read more)

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