RSofia-package: Train and Test Suite of Models from Sofia-ml

Description

Sofia-ml is a suite of fast incremental algorithms for machine learning that can be used for training models for classification or ranking

Arguments

Details

Package:

RSofia

Type:

Package

Version:

1.0

Date:

2011-09-06

License:

apache

LazyLoad:

yes

References

D. Sculley. Combined Regression and Ranking. Proceedings of the 16th Annual SIGKDD Conference on Knowledge Discover and Data Mining, 2010.

D. Sculley. Web-Scale K-Means Clustering. Proceedings of the 19th international conference on World Wide Web, 2010.

D. Sculley. Large Scale Learning to Rank. NIPS Workshop on Advances in Ranking, 2009. Presents the indexed sampling methods used learning to rank, including the rank and roc loops.

K. Crammer, O. Dekel, J. Keshet, S. Shalev-Shwartz, and Y. Singer. Online passive-aggressive algorithms. J. Mach. Learn. Res., 7, 2006. Presents the Passive-Aggressive Perceptron algorithm.

T. Joachims. Optimizing search engines using clickthrough data. In KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, 2002. Presents the RankSVM objective function, a pairwise objective function used by the rank loop method in sofia-ml.

Y. Li and P. M. Long. The relaxed online maximum margin algorithm. Mach. Learn., 46(1-3), 2002. Presents the ROMMA algorithm.

S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: Primal estimated sub-gradient solver for SVM. In ICML '07: Proceedings of the 24th international conference on Machine learning, 2007. Presents the Pegasos SVM solver.

T. Zhang. Solving large scale linear prediction problems using stochastic gradient descent algorithms. In ICML '04: Proceedings of the twenty-first international conference on Machine learning, 2004. Presents SGD SVM.

http://leon.bottou.org/projects/sgd Leon Bottou's SGD page, including experiments with SGD SVM.

W. Krauth and M. M'ezard. Learning algorithms with optimal stability in neural networks. Journal of Physics A, 20(11):745-752, 1987. Presents Perceptron with Margins.