About: Probabilistic performance evaluation for multiclass classification using the posterior balanced accuracy

Readme added. Explanation of the examples in the readme file.

About: Soltion developed by team Turtle Tamers in the ChaLearn Gesture Challenge (http://www.kaggle.com/c/GestureChallenge2)

Initial Announcement on mloss.org.

About: LIBOL is an open-source library that consists of a family of state-of-the-art online learning algorithms for machine learning and data mining research.

Initial Announcement on mloss.org.

About: This package is an implementation of a linear svm solver with a wide class of regularizations on the svm weight vector (l1, l2, mixed norm l1-lq, adaptive lasso). We provide solvers for the classical single task svm problem and for multi-task with joint feature selection or similarity promoting term.

Initial Announcement on mloss.org.

About: PLEASD: A Matlab Toolbox for Structured Learning

Initial Announcement on mloss.org.

About: Message passing for topic modeling

1. improve "readme.pdf".
2. correct some compilation errors.

About: This local and parallel computation toolbox is the Octave and Matlab implementation of several localized Gaussian process regression methods: the domain decomposition method (Park et al., 2011, DDM), partial independent conditional (Snelson and Ghahramani, 2007, PIC), localized probabilistic regression (Urtasun and Darrell, 2008, LPR), and bagging for Gaussian process regression (Chen and Ren, 2009, BGP). Most of the localized regression methods can be applied for general machine learning problems although DDM is only applicable for spatial datasets. In addition, the GPLP provides two parallel computation versions of the domain decomposition method. The easiness of being parallelized is one of the advantages of the localized regression, and the two parallel implementations will provide a good guidance about how to materialize this advantage as software.

Initial Announcement on mloss.org.