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About: MultiBoost is a multi-purpose boosting package implemented in C++. It is based on the multi-class/multi-task AdaBoost.MH algorithm [Schapire-Singer, 1999]. Basic base learners (stumps, trees, products, Haar filters for image processing) can be easily complemented by new data representations and the corresponding base learners, without interfering with the main boosting engine. Changes:Small bug related to linking order is corrected.
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About: This project is a C++ toolkit containing machine learning algorithms and tools that facilitate creating complex software in C++ to solve real world problems. Changes:This release has focused mostly on minor usability and feature improvements. Some highlights are better support for learning to do sequence labeleing from unbalanced data, new image processing routines, and new tools for performing Kalman filtering and recursive least squares filtering.
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About: Link Prediction Made Easy Changes:v1.2.2 - Fixed issue related to Ubuntu g++ header inclusion. This should fix compiler errors encountered by Ubuntu users. - Fixed issue related to the specific command "predict -n 0 -d I" that would cause existing edges to receive predictions.
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About: A broad collection of script-friendly command-line tools for machine learning and data mining tasks. (The command-line tools wrap functionality from a C++ class library.) Changes:See the change log at http://waffles.sourceforge.net/changelog.html
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About: Locally Weighted Projection Regression (LWPR) is a recent algorithm that achieves nonlinear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its [...] Changes:Version 1.2.4
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About: Infrastructure for representing, manipulating and analyzing transaction data and frequent patterns. Changes:Initial Announcement on mloss.org.
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About: MSVMpack is a Multi-class Support Vector Machine (M-SVM) package. It is dedicated to SVMs which can handle more than two classes without relying on decomposition methods and implements the four M-SVM models from the literature: Weston and Watkins M-SVM, Crammer and Singer M-SVM, Lee, Lin and Wahba M-SVM, and the M-SVM2 of Guermeur and Monfrini. Changes:
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About: The scikit-learn aims to provide state of the art standard machine learning algorithms in Python. Changes:Initial Announcement on mloss.org.
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About: The SSA Toolbox is an efficient, platform-independent, standalone implementation of the Stationary Subspace Analysis algorithm with a friendly graphical user interface and a bridge to Matlab. Stationary Subspace Analysis (SSA) is a general purpose algorithm for the explorative analysis of non-stationary data, i.e. data whose statistical properties change over time. SSA helps to detect, investigate and visualize temporal changes in complex high-dimensional data sets. Changes:
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About: Mulan is an open-source Java library for learning from multi-label datasets. Multi-label datasets consist of training examples of a target function that has multiple binary target variables. This means that each item of a multi-label dataset can be a member of multiple categories or annotated by many labels (classes). This is actually the nature of many real world problems such as semantic annotation of images and video, web page categorization, direct marketing, functional genomics and music categorization into genres and emotions. Changes:Learners
Measures
Bug fixes
API changes
Experiments
Examples
Unit Tests
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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 major enhancements, cleanups and bugfixes: Features
Bugfixes
Cleanup and API Changes
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About: CARP: The Clustering Algorithms’ Referee Package Changes:Added generalized overlap, more metrics for comparing partitionings
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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:Release 0.3.0 bumps the version number because the license has changed: instead of the former GPL v2+ license, libDAI is now licensed under the BSD 2-clause license (also known as the FreeBSD license). Further, various bugs have been fixed.
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About: The GPML toolbox is a flexible and generic Octave 3.2.x and Matlab 7.x implementation of inference and prediction in Gaussian Process (GP) models. Changes:We now support inference on large datasets using the FITC approximation by Ed Snelson. The covariance function interface had to be slightly modified.
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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: The DL-Learner framework contains several algorithms for supervised concept learning in Description Logics (DLs) and OWL. Changes:See http://dl-learner.org/wiki/ChangeLog.
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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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