CAREER: An Information-Theoretic Approach to Computational Learning with Applications

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Information theory is the science of quantifying, encoding, andextracting information. The research proposed in this careerdevelopment plan consists of work designed to strengthen thetheoretical foundations and empirical applicability of computationallearning through the development of a rigorous, information-theoreticframework for investigating this discipline. Specifically, aninformation-theoretic framework, based on communication theory, isproposed for investigating the probably approximately correct (PAC)model of machine learning. This framework is shown to be robust andextensible; new learning algorithms, general measures of performance,and analysis techniques are all proposed and demonstrated.In addition to providing a means for strengthening the theoreticalfoundations of computational learning, this framework also provides amechanism for developing and rigorously analyzing new learningalgorithms. New algorithms for hypothesis boosting, documentclassification, document filtering, and meta-search are all proposed.The proposed applications work has both theoretical and experimentalcomponents: all new algorithms are to be analyzed, implemented andtested using benchmark data.Teaching and education are an integral part of this career developmentplan. Both graduate and undergraduate students are introduced totopical research, both theoretical and applied, through the projectsproposed. New courses on learning and information retrieval,accessible to both graduate and undergraduate students, are alsoproposed.