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About: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields. Changes:libDAI release 0.2.7 is a bug-fix release which fixes a bug in the junction-tree MAP inference which could yield incorrect results in some cases. This release accompanies a JMLR open source track publication about libDAI.
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About: The SUMO Toolbox is a Matlab toolbox that automatically builds accurate surrogate models (also known as metamodels or response surface models) of a given data source (e.g., simulation code, data set, script, ...) within the accuracy and time constraints set by the user. The toolbox minimizes the number of data points (which it selects automatically) since they are usually expensive. Changes:Incremental update, fixing some cosmetic issues, coincides with JMLR publication.
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About: Model-Based Boosting Changes:Fetched by r-cran-robot on 2010-08-12 12:52:48.210419
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About: The SHOGUN machine learning toolbox's focus is on large scale learning methods with focus on Support Vector Machines (SVM), providing interfaces to python, octave, matlab, r and the command line. Changes:This release contains several enhancements, cleanups and bugfixes: Features
Bugfixes
Cleanup and API Changes
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About: Massive Online Analysis (MOA) is a real time analytic tool for data streams. It is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and it is released under the GNU GPL license. Changes:Initial Announcement on mloss.org.
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About: The library is focused on implementation of propagation based approximate inference methods. Also implemented are a clique tree based exact inference, Gibbs sampling, and the mean field algorithm. Changes:Initial Announcement on mloss.org.
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About: The CTBN-RLE is a C++ package of executables and libraries for inference and learning algorithms for continuous time Bayesian networks (CTBNs). Changes:Minor code changes (a few compilation issues, #define .h guard name changes).
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About: This toolbox provides functions for maximizing and minimizing submodular set functions, with applications to Bayesian experimental design, inference in Markov Random Fields, clustering and others. Changes:
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About: The open source Error-Correcting Output Codes (ECOC) library contains both state-of-the-art coding and decoding designs, as well as the option to include your own coding, decoding, and base classifier. Changes:Initial Announcement on mloss.org.
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About: PyBrain is a versatile machine learning library for Python. Its goal is to provide flexible, easy-to-use yet still powerful algorithms for machine learning tasks, including a variety of predefined [...] Changes:
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About: RL-Glue allows agents, environments, and experiments written in Java, C/C++, Matlab, Python, and Lisp to inter operate, accelerating research by promoting software re-use in the community. Changes:RL-Glue paper has been published in JMLR.
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About: Model Monitor is a Java toolkit for the systematic evaluation of classifiers under changes in distribution. It provides methods for detecting distribution shifts in data, comparing the performance [...] Changes:Improved AUROC calculation. Several minor bug fixes.
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About: Nieme is a C++ machine learning library for large-scale classification, regression and ranking. It provides a simple interface available in C++, Python and Java and a user interface for visualization. Changes:Released Nieme 1.0
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About: Pebl is a python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations. Changes:Updated version to 1.0.1
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About: JNCC2 is the open-source implementation of the Naive Credal Classifier2 (NCC2), i.e., an extension of Naive Bayes towards imprecise probabilities, designed to deliver robust classifications even on [...] Changes:Initial Announcement on mloss.org.
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About: LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, L1-loss linear SVM, and multi-class SVM Changes:Initial Announcement on mloss.org.
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