About: A fast implementation of several stochastic gradient descent learners for classification, ranking, and ROC area optimization, suitable for large, sparse data sets. Includes Pegasos SVM, SGD-SVM, Passive-Aggressive Perceptron, Perceptron with Margins, Logistic Regression, and ROMMA. Commandline utility and API libraries are provided.

Initial Announcement on mloss.org.

About: LibSGDQN proposes an implementation of SGD-QN, a carefully designed quasi-Newton stochastic gradient descent solver for linear SVMs.

small bug fix (thx nicolas ;)

About: CRFSuite is a speed-oriented implementation of Conditional Random Fields (CRFs). This software features: parameter estimation using SGD and L-BFGS, l1/l2 regularization, simple data I/O format, etc.

Initial Announcement on mloss.org.

About: The SGD package contains a stochastic gradient implementation of linear SVMs and linear CRFs. It demonstrate that a simple stochastic gradient descent is very competitive algorithm for such tasks. [...]

Initial Announcement on mloss.org.