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

• General:
  • Made code able to compile under both Java 1.6 and 1.7. This required removing some potentially unsafe methods that used varargs with generics.
  • Upgraded XStream dependency to 1.4.4.
  • Improved support for regression algorithms in learning.
  • Added general-purpose adapters to make it easier to compose learning algorithms and adapt their input or output.
• Common Core:
  • Added isSparse, toArray, dotDivide, and dotDivideEquals methods for Vector and Matrix.
  • Added scaledPlus, scaledPlusEquals, scaledMinus, and scaledMinusEquals to Ring (and thus Vector and Matrix) for potentially faster such operations.
  • Fixed issue where matrix and dense vector equals was not checking for equal dimensionality.
  • Added transform, transformEquals, tranformNonZeros, and transformNonZerosEquals to Vector.
  • Made LogNumber into a signed version of a log number and moved the prior unsigned implementation into UnsignedLogNumber.
  • Added EuclideanRing interface that provides methods for times, timesEquals, divide, and divideEquals. Also added Field interface that provides methods for inverse and inverseEquals. These interfaces are now implemented by the appropriate number classes such as ComplexNumber, MutableInteger, MutableLong, MutableDouble, LogNumber, and UnsignedLogNumber.
  • Added interface for Indexer and DefaultIndexer implementation for creating a zero-based indexing of values.
  • Added interfaces for MatrixFactoryContainer and DivergenceFunctionContainer.
  • Added ReversibleEvaluator, which various identity functions implement as well as a new utility class ForwardReverseEvaluatorPair to create a reversible evaluator from a pair of other evaluators.
  • Added method to create an ArrayList from a pair of values in CollectionUtil.
  • ArgumentChecker now properly throws assertion errors for NaN values. Also added checks for long types.
  • Fixed handling of Infinity in subtraction for LogMath.
  • Fixed issue with angle method that would cause a NaN if cosine had a rounding error.
  • Added new createMatrix methods to MatrixFactory that initializes the Matrix with the given value.
  • Added copy, reverse, and isEmpty methods for several array types to ArrayUtil.
  • Added utility methods for creating a HashMap, LinkedHashMap, HashSet, or LinkedHashSet with an expected size to CollectionUtil.
  • Added getFirst and getLast methods for List types to CollectionUtil.
  • Removed some calls to System.out and Exception.printStackTrace.
• Common Data:
  • Added create method for IdentityDataConverter.
  • ReversibleDataConverter now is an extension of ReversibleEvaluator.
• Learning Core:
  • Added general learner transformation capability to make it easier to adapt and compose algorithms. InputOutputTransformedBatchLearner provides this capability for supervised learning algorithms by composing together a triplet. CompositeBatchLearnerPair does it for a pair of algorithms.
  • Added a constant and identity learners.
  • Added Chebyshev, Identity, and Minkowski distance metrics.
  • Added methods to DatasetUtil to get the output values for a dataset and to compute the sum of weights.
  • Made generics more permissive for supervised cost functions.
  • Added ClusterDistanceEvaluator for taking a clustering that encodes the distance from an input value to all clusters and returns the result as a vector.
  • Fixed potential round-off issue in decision tree splitter.
  • Added random subspace technique, implemented in RandomSubspace.
  • Separated functionality from LinearFunction into IdentityScalarFunction. LinearFunction by default is the same, but has parameters that can change the slope and offset of the function.
  • Default squashing function for GeneralizedLinearModel and DifferentiableGeneralizedLinearModel is now a linear function instead of an atan function.
  • Added a weighted estimator for the Poisson distribution.
  • Added Regressor interface for evaluators that are the output of (single-output) regression learning algorithms. Existing such evaluators have been updated to implement this interface.
  • Added support for regression ensembles including additive and averaging ensembles with and without weights. Added a learner for regression bagging in BaggingRegressionLearner.
  • Added a simple univariate regression class in UnivariateLinearRegression.
  • MultivariateDecorrelator now is a VectorInputEvaluator and VectorOutputEvaluator.
  • Added bias term to PrimalEstimatedSubGradient.
• Text Core:
  • Fixed issue with the start position for tokens from LetterNumberTokenizer being off by one except for the first one.

About: A scalable, fast C++ machine learning library, with emphasis on usability.

Speedups of cover tree traversers; addition of rank-approximate nearest neighbor (RANN); addition of fast exact max-kernel search (FastMKS); fix for EM covariance estimation; more parameters for GMM estimation; force GMM and GaussianDistribution covariance matrices to be positive definite during training; add a tolerance parameter to the Baum-Welch algorithm for HMM training; fix for compilation with clang; fix for k-furthest neighbor search.

About: A descriptive and programming language independent format and API for the simplified configuration, documentation, and design of computer experiments.

Initial Announcement on mloss.org.

About: a dbms for resonating neural networks. Create and use different types of machine learning algorithms.

AIML compatible (AIML files can be imported); new 'Grid channel' for developing board games; improved topics editor; new demo project: ALice (from AIML); lots of bug-fixes and speed improvements

About: Use the power of crowdsourcing to create ensembles.

Initial Announcement on mloss.org.

About: Moses is a statistical machine translation system that allows you to automatically train translation models for any language pair. All you need is a collection of translated texts (parallel corpus). An efficient search algorithm finds quickly the highest probability translation among the exponential number of choices.

Initial Announcement on mloss.org.