About: A library of scalable Bayesian generalised linear models with fancy features

• 1.0 release!
• Now there is a random search phase before optimization of all hyperparameters in the regression algorithms. This improves the performance of revrand since local optima are more easily avoided with this improved initialisation
• Regression regularizers (weight variances) associated with each basis object, this approximates GP kernel addition more closely
• Random state can be set for all random objects
• Numerous small improvements to make revrand production ready
• Final report
• Documentation improvements

About: SALSA (Software lab for Advanced machine Learning with Stochastic Algorithms) is an implementation of the well-known stochastic algorithms for Machine Learning developed in the high-level technical computing language Julia. The SALSA software package is designed to address challenges in sparse linear modelling, linear and non-linear Support Vector Machines applied to large data samples with user-centric and user-friendly emphasis.

Initial Announcement on mloss.org.

About: Efficient implementation of Semi-Stochastic Gradient Descent algorithm (S2GD) for training logistic regression (L2-regularized).

Initial Announcement on mloss.org.

About: The SGD-2.0 package contains implementations of the SGD and ASGD algorithms for linear SVMs and linear CRFs.

Version 2.0 features ASGD.

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.