Dec 31, 2009 · Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations to support interpretability and scalability.

Feb 24, 2010 · Non-Sparse Regularization and Efficient Training with. Multiple ... subsumes state-of-the-art approaches to multiple kernel learning, covering ...

An experiment on controlled artificial data experiment sheds light on the appropriateness of sparse, non-sparse and ∞ MKL in various scenarios. Application of p ...

Feb 27, 2010 · Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations to support interpretability and scalability.

Feb 23, 2010 · Learning with Kernels. MIT Press, Cambridge, MA, 2002. B. Schölkopf, A. Smola, and K.-R. Müller. Nonlinear component analysis as a kernel eigen-.

To allow for robust kernel mixtures, we generalize MKL to arbitrary norms. We devise new insights on the connection between several existing MKL formulations ...

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In this paper, we give a new generalization error bound of Multiple Kernel Learn- ing (MKL) for a general class of regularizations.

This page shows the traffic from major research institutions for Non-Sparse Regularization and Efficient Training with Multiple Kernels on Sciweavers sorted ...

Abstract. Recent advances in Multiple Kernel Learn- ing (MKL) have positioned it as an attrac- tive tool for tackling many supervised learn- ing tasks.