**Support Vector Machines
in the primal**

- O. Chapelle, Training a Support Vector Machine in the Primal, Neural Computation, in press.
- Matlab code (with an example). It should be faster than standard SVM solvers in the linear case (the complexity is linear in the number of training examples) and comparable in the nonlinear case (but it requires the whole kernel matrix to fit in memory).
- For a C++ implementation of a primal linear SVM, you can use the SVMlin package (designed more generally for semi-supervised learning).

- S. S. Keerthi, O. Chapelle, D. DeCoste, Building Support Vector Machines with Reduced Classifier Complexity, JMLR 7, 2006.
- Matlab code and example (requires the kernel function).

RankSVM

RankSVM is an algorithm to learn preferences:- It is described in the papers of T. Joachims and R. Herbrich et al.
- It is a straightforward modification from SVM for classification. Here is the corresponding Matlab code.
- You can test it on the Ohsumed dataset from the Letor distribution: test code, Matlab version of the Ohsumed dataset.

Send me an email if you have any question.