Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Convergence Rate of Multiple Kernel Learning with Elastic-net
  Regularization   We investigate the learning rate of multiple kernel leaning (MKL) with
elastic-net regularization, which consists of an $\ell_1$-regularizer for
inducing the sparsity and an $\ell_2$-regularizer for controlling the
smoothness. We focus on a sparse setting where the total number of kernels is
large but the number of non-zero components of the ground truth is relatively
small, and prove that elastic-net MKL achieves the minimax learning rate on the
$\ell_2$-mixed-norm ball. Our bound is sharper than the convergence rates ever
shown, and has a property that the smoother the truth is, the faster the
convergence rate is.

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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: chem-ph
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The categories of this paper are: solv-int
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The categories of this paper are: gr-qc
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The categories of this paper are: adap-org
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The categories of this paper are: nlin
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The categories of this paper are: acc-phys
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The categories of this paper are: econ
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The categories of this paper are: hep-th
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The categories of this paper are: physics
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The categories of this paper are: comp-gas
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-lat