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Robert E. Schapire

Robert E. Schapire is Professor of Computer Science at Princeton University. For their work on boosting, Freund and Schapire received both the Gödel Prize in 2003 and the Kanellakis Theory and Practice Award in 2004.

Titles by This Author

Foundations and Algorithms

Boosting is an approach to machine learning based on the idea of creating a highly accurate predictor by combining many weak and inaccurate “rules of thumb.” A remarkably rich theory has evolved around boosting, with connections to a range of topics, including statistics, game theory, convex optimization, and information geometry. Boosting algorithms have also enjoyed practical success in such fields as biology, vision, and speech processing. At various times in its history, boosting has been perceived as mysterious, controversial, even paradoxical.