SMIDAS 1.1

Shai Shalev Shwartz, Ambuj Tewari — Sun, 15 Aug 2010 18:51:51 -0000

SMIDAS is a C++ implementation of the stochastic mirror descent algorithm proposed in

* Shai Shalev-Shwartz and Ambuj Tewari, Stochastic methods for l1 regularized loss minimization. Proceedings of the 26th International Conference on Machine Learning, pages 929-936, 2009.

It can be used for l1-regularized loss minimization for both classification and regression problems.

Currently supported loss functions are the logistic loss, the hinge loss, and the squared loss [L(a,b) = (a-b)2]. SMIDAS is designed to run fast even for high-dimensional large datasets and can exploit the sparsity in the examples.

mloss.org SMIDAS

SMIDAS 1.1