About: A stochastic variant of the mirror descent algorithm employing Langford and Zhang's truncated gradient idea to minimize L1 regularized loss minimization problems for classification and regression.

Fixed major bug in implementation. The components of the iterate where the current example vector is zero were not being updated correctly. Thanks to Jonathan Chang for pointing out the error to us.

About: A (randomized) coordinate descent procedure to minimize L1 regularized loss for classification and regression purposes.

Fixed some I/O bugs. Lines that ended with whitespace were not read correctly in the previous version.

About: The software solves the SVM optimization problem.

Initial Announcement on mloss.org.