SCD 2.1

Shai Shalev Shwartz, Ambuj Tewari — Thu, 03 Dec 2009 22:21:45 -0000

SCD is a C++ implementation of the stochastic coordinate descent algorithm proposed in

* Shai Shalev-Shwartz and Ambuj Tewari, Stochastic methods for l1 regularized loss minimization. Submitted to Journal of Machine Learning Research

which, in turn, is a modification of the original stochastic coordinate 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 and the squared loss. SCD is designed to run fast even for large high-dimensional datasets and can exploit the sparsity in the examples.

mloss.org SCD

SCD 2.1