Description:

Investigation of dependencies between multiple data sources allows the discovery of regularities and interactions that are not seen in individual data sets. The increasing availability of co-occurring measurement data in computational biology, social sciences, and in other domains emphasizes the need for practical implementations of general-purpose dependency modeling algorithms.

The project collects various dependency modeling approaches into a unified toolbox. The techniques for the discovery and analysis of statistical dependencies are based on well-established models such as probabilistic canonical correlation analysis and multi-task learning whose applicability has been demonstrated in previous case studies.

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

Supported Operating Systems: Platform Independent

Data Formats: Agnostic

Tags: Bioinformatics, Machine Learning, Statistics, Learning Principles, Probabilistic Models, Icml2010

Archive: download here

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