About: The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modelling, together with graphical user interfaces for easy access to this [...]

http://sourceforge.net/projects/weka/files/weka-3-7/3.7.6/README-3-7-6.txt/view

About: ELKI is a framework for implementing data-mining algorithms with support for index structures, that includes a wide variety of clustering and outlier detection methods.

The full changelog is not yet up. Here is an excerpt of the new functions in 0.5.0 - further speed improvements - R-Tree flexibility: multiple new split strategies, bulk loaders, insertion strategies, so that ELKI can now do many R-Tree variations, including the original Guttman R-Tree, not only the R*-Tree. - K-Means flexibility: MacQueen and Lloyd style iterations along with various seeding strategies, including K-Means++ - VA-File (static only, not dynamic databases); partial-VA to come for 0.5.0 final? - Many popular cluster evaluation measures - Alpha shapes, Voronoi cells, Delaunay triangulations in the visualization layer (in the projected space, so 2D!) - Parallel coordinates (only halfway reviewed in beta1, more to come!) - Outlier ensemble code, to be presented at SDM 2012 end of april

For the final 0.5.0 release we hope to have some approximate outlier detection methods for you (aLOCI, HilOut) as well as some subspace outlier detection methods including HiCS (ICDE 2012, to be presented tomorrow).

About: Use the power of crowdsourcing to create ensembles.

Initial Announcement on mloss.org.

About: MLwizard recommends and optimizes classification algorithms based on meta-learning and is a software wizard fully integrated into RapidMiner but can be used as library as well.

Initial Announcement on mloss.org.

About: Oboe is a software for Chinese syntactic parsing, and it can display syntactic trees in a graphical view with two kinds of representation: phrase tree and dependency tree. So it is very helpful for NLP researchers, especially for researchers focusing on syntax-based methods.

Initial Announcement on mloss.org.

About: Motivated by a need to classify high-dimensional, heterogeneous data from the bioinformatics domain, we developed ML-Flex, a machine-learning toolbox that enables users to perform two-class and multi-class classification analyses in a systematic yet flexible manner. ML-Flex was written in Java but is capable of interfacing with third-party packages written in other programming languages. It can handle multiple input-data formats and supports a variety of customizations. MLFlex provides implementations of various validation strategies, which can be executed in parallel across multiple computing cores, processors, and nodes. Additionally, ML-Flex supports aggregating evidence across multiple algorithms and data sets via ensemble learning. (See http://jmlr.csail.mit.edu/papers/volume13/piccolo12a/piccolo12a.pdf.)

Initial Announcement on mloss.org.

About: 3-layer neural network for regression with sigmoid activation function and command line interface similar to LibSVM.

Initial Announcement on mloss.org.

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: An annotated java framework for machine learning, aimed at making it really easy to access analytically functions.

Now supports OLS and GLS regression and NaiveBayes classification

About: Multicore/distributed large scale machine learning framework.

Update version.

About: The Cognitive Foundry is a modular Java software library of machine learning components and algorithms designed for research and applications.

• Common Core:
  • Added checkedAdd and checkedMultiply functions to MathUtil, providing a means for conducting Integer addition and multiplication with explicit checking for overflow and underflow, and throwing an ArithmeticException if they occur. Java fails silently in integer over(under)flow situations.
  • Added explicit integer overflow checks to DenseMatrix. The underlying MTJ library stores dense matrices as a single dimensional arrays of integers, which in Java are 32-bit. When creating a matrix with numRows rows and numColumns columns, if numRows * numColumns is more than 2^31 - 1, a silent integer overflow would occur, resulting in later ArrayIndexOutOfBoundsExceptions when attempting to access matrix elements that didn't get allocated.
  • Added new methods to DiagonalMatrix interface for multiplying diagonal matrices together and for inverting a DiagonalMatrix.
  • Optimized operations on diagonal matrices in DiagonalMatrixMTJ.
  • Added checks to norm method in AbstractVectorSpace and DefaultInfiniteVector for power set to NaN, throwing an ArithmeticException if encountered.
• Learning Core:
  • Optimized matrix multiplies in LogisticRegression to avoid creating dense matrices unnecessarily and to reduce computation time using improved DiagonalMatrix interfaces.
  • Added regularization and explicit bias estimation to MultivariateLinearRegression.
  • Added ConvexReceiverOperatingCharacteristic, which computes the convex hull of the ROC.
  • Fixed rare corner-case bug in ReceiverOperatingCharacteristic and added optional trapezoidal AUC computation.
  • Cleaned up constant in MultivariateCumulativeDistributionFunction and added publication references.

About: A decision tree learner that is designed to be reasonably fast, but the primary goal is ease of use

Initial Announcement on mloss.org.

About: MLPlot is a lightweight plotting library written in Java.

Initial Announcement on mloss.org.

About: The K-tree is a scalable approach to clustering inspired by the B+-tree and k-means algorithms.

Release of K-tree implementation in Python. This is targeted at a research and rapid prototyping audience.

About: KReator is an integrated development environment (IDE) for relational probabilistic knowledge representation languages. At the moment, KReator supports Bayesian Logic Programs (BLPs), Markov Logic Networks (MLNs), Relational Maximum Entropy (RME), Relational Bayesian Networks (RBN), and Probabilistic Prolog (ProbLog).

• several bugfixes
• Beta version of ProbLog plugin
• enhanced command completion in console
• enhanced error messages for syntax errors
• refactored logic libraries
• added prettyprint function in console
• added syntax highlighting for RBNs

About: Apache Mahout is an Apache Software Foundation project with the goal of creating both a community of users and a scalable, Java-based framework consisting of many machine learning algorithm [...]

We are pleased to announce release 0.4 of Mahout. Virtually every corner of the project has changed, and significantly, since 0.3. Developers are invited to use and depend on version 0.4 even as yet more change is to be expected before the next release. Highlights include:

* Model refactoring and CLI changes to improve integration and consistency
* New ClusterEvaluator and CDbwClusterEvaluator offer new ways to evaluate clustering effectiveness
* New Spectral Clustering and MinHash Clustering (still experimental)
* New VectorModelClassifier allows any set of clusters to be used for classification
* Map/Reduce job to compute the pairwise similarities of the rows of a matrix using a customizable similarity measure
* Map/Reduce job to compute the item-item-similarities for item-based collaborative filtering
* RecommenderJob has been evolved to a fully distributed item-based recommender
* Distributed Lanczos SVD implementation
* More support for distributed operations on very large matrices
* Easier access to Mahout operations via the command line
* New HMM based sequence classification from GSoC (currently as sequential version only and still experimental)
* Sequential logistic regression training framework
* New SGD classifier
* Experimental new type of NB classifier, and feature reduction options for existing one
* New vector encoding framework for high speed vectorization without a pre-built dictionary
* Additional elements of supervised model evaluation framework
* Promoted several pieces of old Colt framework to tested status (QR decomposition, in particular)
* Can now save random forests and use it to classify new data
* Many, many small fixes, improvements, refactorings and cleanup

About: This Java software implements Profile Hidden Markov Models (PHMMs) for protein classification for the WEKA workbench. Standard PHMMs and newly introduced binary PHMMs are used. In addition the software allows propositionalisation of PHMMs.

description changed

About: KeplerWeka represents the integration of all the functionality of the WEKA Machine Learning Workbench into the open-source scientific workflow Kepler. Among them are classification, [...]

• Now compatible with Kepler 2.0
• New version of WEKA included (patched 3.7.2 release), WEKA's new package manager works in conjunction with Kepler
• Renamed actor Count to ConditionalTee, introduced new Count actor
• Removed actors OutputLogger, MultiSync, TwinSync