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: BayesOpt is an efficient, C++ implementation of the Bayesian optimization methodology for nonlinear-optimization, experimental design and stochastic bandits. In the literature it is also called Sequential Kriging Optimization (SKO) or Efficient Global Optimization (EGO). There are also interfaces for C, Matlab/Octave and Python.

-Fixed bugs.

-Improved and extended documentation.

-Extended and simplified API accross platforms.

-Extended functionality (new surrogate functions, new priors, new kernels, new criteria).

-Improved modularity of the optimization process to allow plotting and debugging of intermediate steps.

-Added more demos and examples.

About: Somoclu is a cluster-oriented implementation of self-organizing maps. It relies on MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes.

Initial Announcement on mloss.org.

About: Robust learning of Bayesian Networks

Initial Announcement on mloss.org.

About: ITE (Information Theoretical Estimators) is capable of estimating many different variants of entropy, mutual information, divergence, association measures and cross quantities. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems.

• K divergence estimation: added,

• L divergence estimation: added,

• kNN squared distance computation: refined.

About: HLearn makes simple machine learning routines available in Haskell by expressing them according to their algebraic structure

Updated to version 1.0

About: This is an optimization library based on Social Impact Theory(SITO). The optimizer works in the same way as PSO and GA.

minor changes

About: A collection of python code to perform research in optimization. The aim is to provide reusable components that can be quickly applied to machine learning problems. Used in: - Ellipsoidal multiple instance learning - difference of convex functions algorithms for sparse classfication - Contextual bandits upper confidence bound algorithm (using GP) - learning output kernels, that is kernels between the labels of a classifier.

• minor bugfix

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: Multivariate Adaptive Regression Spline Models

Fetched by r-cran-robot on 2013-05-01 00:00:06.060849