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Learning Interpretable SVMs for biological Sequence Analysis

Supplementary pages for the BMC Bioinformatics paper "Learning Interpretable SVMs for biological Sequence Analysis" by Sören Sonnenburg, Gunnar Rätsch and Christin Schäfer.

Please note:

The paper is available for download here.

The supplementary web pages are available here.

We have generalized the MKL approach in a NIPS'06 contribution A General and Efficient Multiple Kernel Learning Algorithm

Moreover, we continued our work on visualizing the SVM decision boundary in our recent work Positional Oligomer Importance Matrices by Sören Sonnenburg, Alexander Zien, Petra Philips and Gunnar Rätsch.

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