Project details for SCD

Logo SCD 2.1

by ambujtewari - December 3, 2009, 22:21:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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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
URL: Project Homepage
Supported Operating Systems: Agnostic
Data Formats: Ascii
Tags: Coordinate Descent, L1 Regularization, Large Datasets
Archive: download here

Other available revisons

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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