Logistic regression with regularization

This matlab code implements classical linear logistic regression, but adds a regularization term that prevents the transition from one class to another to be too sharp, i.e. it penalizes the slope of the transition. As we explain in Appendinx B of the paper cited blow, this approximates support vectors when the classes are well separated. If classes do have many points that overlap, logistic regression assumes a smooth transition between the two classes, wheras support vectors do not make much sense in that case. In a way this code implements the best of both worlds.

Lucas C. Parra, Clay D. Spence, Adam D. Gerson, Paul Sajda, "Recipes for the Linear Analysis of EEG", Neuroimage, vol. 28, no. 2, pp. 326-341, November 2005.