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About: Piqle (Platform for Implementing QLearning Experiments) is a Java framework for fast design, prototyping and test of reinforcement learning experiments (RL). By clearly separating algorithms and problems, it allows users to focus on either part of the RL paradigm:designing new algorithms or implementing new problems. Piqle implements many classical RL algorithms, making their parameters easily tunable. At this time, 13 problems are implemented, several with one or more variants. The user's manual explains in detail how to code a new problem. Written in Java, Piqle is as platformindependent as Java itself. Its components can easily be embedded as part of complex implementations, like robotics or decision making. Changes:Initial Announcement on mloss.org.

About: Preparing Changes:Initial Announcement on mloss.org.

About: ARTS is an accurate predictor for Transcription Start Sites (TSS). Changes:Initial Announcement on mloss.org.

About: OLL is a library supporting several for onlinelearning algorithms, which provides C++ library, and standalone programs for learning, predicting. OLL is specialized for largescale, but sparse, [...] Changes:Initial Announcement on mloss.org.

About: Ohmm is a library for learning hidden Markov models by using Online EM algorithm. This library is specialized for large scale data; e.g. 1 million words. The output includes parameters, and estimation results. Changes:Initial Announcement on mloss.org.

About: C++ Library for Highlevel Computer Vision Tasks Changes:Initial Announcement on mloss.org.

About: CRF++ is a simple, customizable, and open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data. Changes:Initial Announcement on mloss.org.

About: BMRM is an open source, modular and scalable convex solver for many machine learning problems cast in the form of regularized risk minimization problem. Changes:Initial Announcement on mloss.org.

About: This package contains functions to estimate linear and nonlinear regression methods with Penalized Partial Least Squares. Changes:

About: The package estimates the matrix of partial correlations based on different regularized regression methods: lasso, adaptive lasso, PLS, and Ridge Regression. Changes:Initial Announcement on mloss.org.
