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- Description:
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.
- Changes to previous version:
Fixed some I/O bugs. Lines that ended with whitespace were not read correctly in the previous version.
- BibTeX Entry: Download
- Corresponding Paper BibTeX Entry: Download
- Supported Operating Systems: Agnostic
- Data Formats: Ascii
- Tags: Coordinate Descent, L1 Regularization, Large Datasets
- Archive: download here
Other available revisons
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Version Changelog Date 2.1 Fixed some I/O bugs. Lines that ended with whitespace were not read correctly in the previous version.
December 3, 2009, 22:21:45 2.0 Instead of keeping a vector of length 2*d as in the previous version, now the algorithm only maintain a vector of length d, where d is the number of features. This slightly reduces both the code length and run-time.
November 27, 2009, 04:03:39 1.0 Initial Announcement on mloss.org.
August 5, 2009, 00:53:40
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