Learning Interpretable SVMs for biological Sequence Analysis

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.