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.

• A new fast (sublinear in the number of instances) stump algorithm is implemented. The gain in time is proportional to the sparsity of the features (it is significant when a lot of instances take the most frequent feature value). See Section B.2 in the documentation.
• A parametrized early stopping option is added in --traintest mode. We stop if the (smoothed) test error does not improve for a certain number of iterations. See Section 4.1.3 in the documentation.

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.

This release also contains several enhancements, cleanups and bugfixes:

Features

• Linear Time MMD two-sample test now works on streaming-features, which allows to perform tests on infinite amounts of data. A block size may be specified for fast processing. The below features were also added. By Heiko Strathmann.
• It is now possible to ask streaming features to produce an instance of streamed features that are stored in memory and returned as a CFeatures* object of corresponding type. See CStreamingFeatures::get_streamed_features().
• New concept of artificial data generator classes: Based on streaming features. First implemented instances are CMeanShiftDataGenerator and CGaussianBlobsDataGenerator. Use above new concepts to get non-streaming data if desired.
• Accelerated projected gradient multiclass logistic regression classifier by Sergey Lisitsyn.
• New CCSOSVM based structured output solver by Viktor Gal
• A collection of kernel selection methods for MMD-based kernel two- sample tests, including optimal kernel choice for single and combined kernels for the linear time MMD. This finishes the kernel MMD framework and also comes with new, more illustrative examples and tests. By Heiko Strathmann.
• Alpha version of Perl modular interface developed by Christian Montanari.
• New framework for unit-tests based on googletest and googlemock by Viktor Gal. A (growing) number of unit-tests from now on ensures basic funcionality of our framework. Since the examples do not have to take this role anymore, they should become more ilustrative in the future.
• Changed the core of dimension reduction algorithms to the Tapkee library.

Bugfixes

• Fix for shallow copy of gaussian kernel by Matt Aasted.
• Fixed a bug when using StringFeatures along with kernel machines in cross-validation which cause an assertion error. Thanks to Eric (yoo)!
• Fix for 3-class case training of MulticlassLibSVM reported by Arya Iranmehr that was suggested by Oksana Bayda.
• Fix for wrong Spectrum mismatch RBF construction in static interfaces reported by Nona Kermani.
• Fix for wrong include in SGMatrix causing build fail on Mac OS X (thanks to @bianjiang).
• Fixed a bug that caused kernel machines to return non-sense when using custom kernel matrices with subsets attached to them.
• Fix for parameter dictionary creationg causing dereferencing null pointers with gaussian processes parameter selection.
• Fixed a bug in exact GP regression that caused wrong results.
• Fixed a bug in exact GP regression that produced memory errors/crashes.
• Fix for a bug with static interfaces causing all outputs to be -1/+1 instead of real scores (reported by Kamikawa Masahisa).

Cleanup and API Changes

• SGStringList is now based on SGReferencedData.
• "confidences" in context of CLabel and subclasses are now "values".
• CLinearTimeMMD constructor changes, only streaming features allowed.
• CDataGenerator will soon be removed and replaced by new streaming- based classes.
• SGVector, SGMatrix, SGSparseVector, SGSparseVector, SGSparseMatrix refactoring: Now contains load/save routines, relevant functions from CMath, and implementations went to .cpp file.

About: The scikit-learn aims to provide state of the art standard machine learning algorithms in Python.

Update for 0.13.1

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.

Learners

• BinaryRelevance.java: improved data handling that avoids copying the entire input space, leading to important speedups in case of large datasets and very large number of labels.
• RAkEL.java: updated technical information, added a check for the case where the number of labels is less or equal than the size of the subset.
• MultiLabelKNN.java: now checks whether the number of instances is less than the number of requested nearest neighbors.
• Addition of AdaBoostMH.java, an explicit implementation of AdaBoost.MH as combination of AdaBoostM1 and IncludeLabelsClassifier.
• Addition of MLPTO.java, the Multi Label Probabilistic Threshold Optimizer (MLTPTO) thresholding technique.
• Addition of ApproximateExampleBasedFMeasureOptimizer.java, an approximate method for the maximization of example-based F-measure.

Measures/Evaluation

• Addition of Specificity measure (example-based, micro/macro label-based)
• Addition of Mean Average Interpolated Precision (MAiP), Geometric Mean Average Precision (GMAP), Geometric Mean Average Interpolated Precision (GMAiP).
• New methods for stratified multi-label evaluation.
• Added support for outputting per label results for all measures that implement the MacroAverageMeasure interface.
• Simplifying the "strictness" issue of information retrieval measures, by adopting specific assumptions (outlined in the new class InformationRetrievalMeasures.java) to handle special cases, instead of the less clear and useful solution of outputting NaN and the less realistic solution or ignoring special cases.

Bug fixes

• Bug fix in LabelsBuilder.java.
• Bug fix in Ranker.java.
• Bug-fix in ThresholdPrediction.java.
• Fix for bug occurring when loading the XSD for mulan data outside the command-line environment (e.g. web applications).
• Javadoc comment updates.

API changes

• Upgrade to Java 1.6
• Upgrade to JUnit 4.10
• Upgrade to Weka 3.7.6.

Miscellaneous

• Meaningful messages are now shown when a DataLoadException is thrown.
• PT6(PT6Transformation.java): renamed to IncludeLabelsTransformation.java.
• MultiLabelInstances now support serialization, as needed by the improved binary relevance transformation.
• BinaryRelevanceAttributeEvaluator.java: updated according to latest BR improvements.

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.

Initial Announcement on mloss.org.

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 [...]

• more documentation, including new tutorials
• new and updated example scripts
• major restructuring of the reinforcement learning part
• homogeneous interface for optimization algorithms
• fast networks (arac) are now in an independent package
• new algorithms, network structures, tools...