Showing papers on "Multiple kernel learning published in 2005"

A General and Efficient Multiple Kernel Learning Algorithm

Abstract: While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lankriet et al. (2004) considered conic combinations of kernel matrices for classification, leading to a convex quadratically constraint quadratic program. We show that it can be rewritten as a semi-infinite linear program that can be efficiently solved by recycling the standard SVM implementations. Moreover, we generalize the formulation and our method to a larger class of problems, including regression and one-class classification. Experimental results show that the proposed algorithm helps for automatic model selection, improving the interpretability of the learning result and works for hundred thousands of examples or hundreds of kernels to be combined.

Learning interpretable SVMs for biological sequence classification

Abstract: We propose novel algorithms for solving the so-called Support Vector Multiple Kernel Learning problem and show how they can be used to understand the resulting support vector decision function. While classical kernel-based algorithms (such as SVMs) are based on a single kernel, in Multiple Kernel Learning a quadratically-constraint quadratic program is solved in order to find a sparse convex combination of a set of support vector kernels. We show how this problem can be cast into a semi-infinite linear optimization problem which can in turn be solved efficiently using a boosting-like iterative method in combination with standard SVM optimization algorithms. The proposed method is able to deal with thousands of examples while combining hundreds of kernels within reasonable time. In the second part we show how this technique can be used to understand the obtained decision function in order to extract biologically relevant knowledge about the sequence analysis problem at hand. We consider the problem of splice site identification and combine string kernels at different sequence positions and with various substring (oligomer) lengths. The proposed algorithm computes a sparse weighting over the length and the substring, highlighting which substrings are important for discrimination. Finally, we propose a bootstrap scheme in order to reliably identify a few statistically significant positions, which can then be used for further analysis such as consensus finding.