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Monday, June 16, 2003


As promised I added links to the papers in the post on the STOC business meeting. Let me say some more words on the winner of the Gödel prize

Valiant developed the concept of PAC (Probably Approximably Correct) learning as roughly where a learner sees a small number of labelled examples from a distribution and with high confidence will generate a hypothesis that with high probability will correctly label instances drawn from the same distribution.

A strong learner has confidence close to 100%; a weak learner has confidence only slightly better than 50%. Schapire, using a technique called boosting, showed how to convert a weak learner to a strong learner. This is a wonderful theoretical result but the algorithm had problems that made it difficult to implement.

In their Gödel prize winning paper, A decision-theoretic generalization of on-line learning and an application to boosting, Freund and Schapire develop the adaboost algorithm that solves many of these issues and has become a staple of the theoretical and practical machine learning community.

Boosting has its own web site where you can find much more information about the algorithms and applications.

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