Description:

The Cognitive Foundry is a modular Java software library for the research and development of cognitive systems. It is primarily designed for research and development to be easy to plug into applications to provide adaptive behaviors.

The main part of the Foundry is the Machine Learning package, which contains reusable components and algorithms for machine learning and statistics. It contains many algorithms for supervised and unsupervised learning as well as statistical modeling. It is interface-centric and uses generics to make it easy to customize to the needs of individual applications.

The Cognitive Foundry's development is led by Sandia National Laboratories and is released under the open source BSD License. It requires Java 1.6.

Changes to previous version:

• Common Core:
  • Added LogMath and LogNumber as utilities for computation involving numbers represented in log-space.
  • Added MutableInteger and MutableLong, which are like Integer and Long but with a mutable value, similar to MutableDouble.
  • UnivariateSummaryStatistics added.
  • Added DefaultIndentifiedValue.
• Learning Core:
  • VectorNaiveBayesCategorizer: evaluateWithDiscriminant now properly normalizes the discriminant, which improves results for things like AUC.
  • PartitionalClusterer: Fixed corner-case bug.
  • Added confidence-weighted online learners based on variance, standard deviation, and AROW. They produce ConfidenceWeightedBinaryCategorizer objects with either diagonal or full covariance matrices.
  • Added balanced versions of bagging and IVoting in CategoryBalancedBaggingLearner and CategoryBalancedIVotingLearner.
  • Added online learners based on ROMMA, AROMMA, Ballseptron, Ramp-loss Passive Aggressive Perceptron, Shifting Perceptron, Forgetron, Projectron, Randomized Budget Perceptron, and Stoptron.
  • Added KernelizableBinaryCategorizerOnlineLearner interface for an online linear binary categorizer that can also be used with a kernel. Also provided abstract class AbstractKernelizableBinaryCategorizerOnlineLearner and AbstractLinearCombinationOnlineLearner for common functionality.
  • Moved KernelPerceptron and KernelAdatron to the new learning.perceptron.kernel package.
  • Added KernelUtil utility class for dealing with kernels and kernel binary categorizers.
  • Refactored statistics to remove getMean() from Distribution due to some distributions not having a meaningful mean and confusion for what the mean meant in some classes.
  • Classes and interfaces interfaces named Scalar renamed to Univariate for clarity.
  • Added getTestStatistic() method to ConfidenceStatistic interface and implemented in existing classes.
  • Added support for multiple comparisons tests, including Bonferroni, Holm, Nemenyi, Shaffer, Sidak, and Tukey-Kramer and updated AnalysisOfVarianceOneWay (ANOVA) and FriedmanConfidence.
  • Added multiple-comparisons experiment classes BlockExperimentComparison and MultipleComparisonExperiment
• Text Core:
  • Renamed ProbabilisticLatentSemanticAnalysis.Transform to ProbabilisticLatentSemanticAnalysis.Result for consistency.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Agnostic, Platform Independent

Data Formats: Matlab, Csv, Xml, Xstream

Tags: Classification, Clustering, Adaboost, Decision Tree Learning, Algorithms, Gaussian Mixture Models, Bagging, Ensemble Methods, Gaussian Processes, Affinity Propagation, Bfgs, Generics, Genetic Algorith

Archive: download here

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