Project details for SMIDAS

Logo SMIDAS 1.0

by ambujtewari - August 5, 2009, 01:02:49 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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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.

Changes to previous version:

Initial announcement on

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Agnostic
Data Formats: Ascii
Tags: L1 Regularization, Large Datasets, Mirror Descent, Sparsity
Archive: download here


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