@incollection{KloBreSonZieLasMue09,
    title = {Efficient and Accurate Lp-Norm Multiple Kernel Learning},
    author = {Marius Kloft and Ulf Brefeld and S\"{o}ren Sonnenburg and Pavel Laskov and Klaus-Robert M\"{u}ller and Alexander Zien},
    booktitle = {Advances in Neural Information Processing Systems 22},
    editor = {Y. Bengio and D. Schuurmans and J. Lafferty and C. K. I. Williams and A. Culotta},
    pages = {997--1005},
    year = {2009},
    publisher = {MIT Press},
	pdf = {http://books.nips.cc/papers/files/nips22/NIPS2009_0879.pdf},
    abstract = {Learning linear combinations of multiple kernels is an
        appealing strategy when the right choice of features is unknown.
        Previous approaches to multiple kernel learning (MKL) promote
        sparse kernel combinations to support interpretability.
        Unfortunately, l1-norm MKL is hardly observed to outperform trivial
        baselines in practical applications. To allow for robust kernel
        mixtures, we generalize MKL to arbitrary lp-norms. We devise new
        insights on the connection between several existing MKL
        formulations and develop two efficient interleaved optimization
        strategies for arbitrary p > 1. Empirically, we demonstrate that
        the interleaved optimization strategies are much faster compared to
        the traditionally used wrapper approaches. Finally, we apply
        lp-norm MKL to real-world problems from computational biology,
        showing that non-sparse MKL achieves accuracies that go beyond the
        state-of-the-art.}
}