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SMIDAS 1.0
by ambujtewari - August 5, 2009, 01:02:49 CET [
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- Description:
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 mloss.org.
- 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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Project details for SMIDAS
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Details
- Version:
- 1.0
- Submitter:
- ambujtewari
- Author(s):
- Shai Shalev Shwartz , Ambuj Tewari
- Posted:
- 5 August 2009
- Last Updated:
- 5 August 2009
- Open Source License:
- gnu gpl
- Programming Language(s):
- c++