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:

• All projects now depend on JUnit 4.8.2 instead of 3.8.2 or 4.6.
• Upgraded to XStream to 1.3.1 (and its dependency xpp3_min to 1.1.4c).
• Common Core:
• KDTree: Fixed two bugs that caused the KDTree to not always return the nearest points in rare cases.
• Learning Core:
• PartitionalClusterer: Added an implementation of a partitional clustering algorithm.
• IncrementalClusterCreator: New interface for a cluster creator that can incrementally update clusters. DefaultIncrementalClusterCreator is a default implementation of the interface that just updates the cluster memberships.
• VectorMeanCentroidClusterCreator: Modified to implement the new IncrementalClusterCreator interface.
• BinaryBaggingLearner: Generalized generics in constructor.
• OnlineBaggingCategorizerLearner: Added an implementation of an online version of the bagging algorithm for building ensembles.
• VotingCategorizerEnsemble: Added an unweighted voting categorization ensemble as a counterpart to WeightedVotingCategorizerEnsemble.
• OnlinePassiveAggressivePerceptron: Added an implementation of the Passive-Aggressive algorithm for binary classification. Also contains PA-I (LinearSoftMargin) and PA-II (QuadraticSoftMargin) variants.

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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