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.
- 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.