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: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields.

Release 0.3.1 fixes various bugs. The issues on 64-bit Windows platforms have been fixed and libDAI now offers full 64-bit support on all supported platforms (Linux, Mac OSX, Windows).

About: A Java framework for statistical analysis and classification of biological sequences

February 2, 2012: Jstacs 2.0 released

Jstacs 2.0 changes many names and the structure of several packages. It is not code-compatible with Jstacs 1.5 and earlier

RESTRUCTURING and RENAMING:

former ScoringFunction, NormalizableScoringFunction, Model

• new base-interface SequenceScore
• new sub-interface StatisticalModel of SequenceScore for all statistical models with sub-iterfaces DifferentiableStatisticalModel and TrainableStatisticalModel
• new interface DifferentiableSequenceScore replaces ScoringFunction
• new interface DifferentiableStatisticalModel replaces NormalizableScoringFunction
• new interface TrainableStatisticalModel replaces Model
• new abstract class AbstractDifferentiableSequenceScore
• new abstract class AbstractDifferentiableStatisticalModel replaces AbstractNormalizableScoringFunction
• new abstract class AbstractTrainableStatisticalModel replaces AbstractModel
• former Models renamed to TrainSM
• former ScoringFunction renamed to DiffSS or DiffSM
• getProbFor removed from TrainableStatisticalModel (former Model) and conceptually replaced by getLogProbFor
• getLogScore(Sequence,int,int) with changed meaning of arguments: getLogScore(Sequence,start,end) instead of getLogScore(Sequence,start,length)
• isTrained() replaced by common method isInitialized()

Parameters and Results

• new super-class of Parameters and Results: AnnotatedEntity
• common list-type for Parameters and Results: AnnotatedEntityList
• Renaming: CollectionParameter -> SelectionParameter, MultiSelectionCollectionParameter -> MultiSelectionParameter, new super-class AbstractSelectionParameter
• major refactoring due to common hierarchy and code-cleanup
• lazy evaluation of Parameter/ParameterSet hierarchies moved from ParameterSet (loadParameters()) to ParameterSetContainer (constructor on class)
• SubclassFinder adapted to lazy evaluation

performance measures

• new abstract super-class AbstractPerformanceMeasure of all performance measures
• new interface NumericalPerformanceMeasure for all performance measures that return a single number (as opposed, e.g., to curves)
• new class PerformanceMeasureParameterSet for a collection of general performance measures
• new class NumericalPerformanceMeasureParameterSet for a collection of NumericalPerformanceMeasures
• used in evaluate-method of AbstractClassifier and in ClassifierAssessments

further changes

• Sample renamed to DataSet
• evaluate and evaluateAll in AbstractClassifier joined
• new class IndependentProductDiffSS as super-class of IndepedentProductDiffSM (former IndependentProductScoringFunction)
• new class UniformDiffSS as super-class of UniformDiffSM (former UniformScoringFunction)

NEW FUNCTIONALITY:

• multi-threaded implementation of Baum-Welch and Viterbi training of hidden Markov models
• new Interface Singleton that can be used for singleton instances to save memory, current examples: DNAAlphabet, DNAAlphabetContainer, ProteinAlphabet
• added ProteinAlphabet
• added possibility to use NaN-values with ContinuousAlphabets
• added ArbitraryFloatSequence including static methods for DataSet creation for cases where double-precision is not needed
• new performance measure MaximumFMeasure
• access to Parameters in ParameterSets and Results in ResultSets by name
• emitDataSet in BayesianNetworkDiffSM
• new static method Time.getTimeInstance that returns UserTime or RealTime depending on availability of shared lib
• SubclassFinder allows for adding own base packages
• new method overlaps() in LocatedSequenceAnnotationWithLength
• AbstractTerminationCondition used in ScoreClassifier and sub-classes
• public method propagateESS in HMMFactory
• new method generateLog in DirichletMRG for drawing log-values
• added DifferentiableStatisticalModelFactory

BUGFIXES/IMPROVEMENTS:

• bugfix in propagation of equivalent sample size in HMMFactory
• bugfix in random initialization of BasicHigherOrderTransition
• improved Alignment implementation
• SafeOutputStream with new static factory method getSafeOutputStream, write methods now work on Objects

DOCUMENTATION:

• improved Javadocs in many classes and packages
• new Cookbook with extensive documentation and explanation

MISC:

• output of NonParsableException more verbose
• Exceptions in multi-threaded code now lead to exit of program instead of only stopping the thread
• update of RServe/RClient

About: Link Prediction Made Easy

v1.2.2

• Fixed MAJOR issue related to github migration several months ago. The original github commit neglected to import empty folders. This caused parts of the project compilation procedure to fail. Any users of LPmade who downloaded the most recent version from github over the last several months would have encountered this build error and should download the most recent version. This change updates the network library makefile to create the empty folders and gets around the issue. Very sorry to anybody that this may have inconvenienced, but thanks for hanging in there if you diagnosed and solved it yourself.

• Fixed issue with auroc on 32-bit architectures that caused integer wraparounds that produced incorrect results.

About: CARP: The Clustering Algorithms’ Referee Package

Added generalized overlap, more metrics for comparing partitionings

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.

Initial Announcement on mloss.org.

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.

RL-Glue paper has been published in JMLR.