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SPegasos

Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al. (2007). This implementation globally replaces all missing values and transforms nominal attributes into binary ones. It also normalizes all attributes, so the coefficients in the output are based on the normalized data. Can either minimize the hinge loss (SVM) or log loss (logistic regression). For more information, see S. Shalev-Shwartz, Y. Singer, N. Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. In: 24th International Conference on MachineLearning, 807-814, 2007.
http://weka.sourceforge.net/doc.packages/SPegasos
GNU General Public License 3
University of Waikato, Hamilton, NZ
Mark Hall
24th binary 2007 loss data regression singer globally nominal variant normalizes missing more values coefficients estimated conference shalev information normalized replaces machinelearning international into logistic minimize ones stochastic srebro transforms pegasos solver shwartz either implementation primal implements hinge method attributes output this also based gradient
Aggregated version Version Update time
1.0 1.0.2 Apr 26, 2012
1.0.1 Apr 24, 2012
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