About: The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some MEX files. The code is fully compatible to both Matlab 7.x and GNU Octave 3.2.x. Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework allowing for both MAP estimation and approximate Bayesian inference.

contributed by George Papandreou:

• preconditioning support in the inf/linsolve_lcg.m CG routine.

• @matConv2 and @matFD2 support different boundary conditions in deblurring

• various mat/@*/diagFAtAFt.m support circulant preconditioning

• bugfixes in nonnegativity option in pls/plsLBFGS.m and pen/penVBNorm.m when used together with EP

• inf/diag_inv_sample.m, a Monte Carlo estimator

gfortran support to pls/lbfgsb/Makefile (thanks to Ernst Kloppenburg)

slight modification to mat/@matFFTN/mvm.m to make it more consistent

simple gradient solver using Barzilai-Borwein step size pls/plsBB.m

About: DAL is an efficient and flexibible MATLAB toolbox for sparse learning/reconstruction based on the augmented Lagrangian method.

• 35% faster group lasso.
• Sparse connectivity inference example added (s_test_hsgl.m).
• Non-negative lasso (thanks to Shigeyuki Oba).
• Uses Mark Tygert's pca.m for SVD (PROPACK is not required anymore).

About: Matlab implementation of variational gaussian approximate inference for Bayesian Generalized Linear Models.

Minor bug fix.

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.