Robert Elias Schapire | |
---|---|
Alma mater | Brown University Massachusetts Institute of Technology |
Known for | AdaBoost |
Awards | Gödel prize (2003) Paris Kanellakis Award (2004) |
Scientific career | |
Fields | Computer Science |
Institutions | Microsoft Research AT&T Labs Princeton University |
Thesis | The design and analysis of efficient learning algorithms (1991) |
Doctoral advisor | Ronald Rivest |
Website | http://rob.schapire.net/ |
Robert Elias Schapire is an American computer scientist, former David M. Siegel '83 Professor in the computer science department at Princeton University, and has recently moved to Microsoft Research. His primary specialty is theoretical and applied machine learning.
His work led to the development of the boosting ensemble algorithm used in machine learning. His PhD dissertation, The design and analysis of efficient learning algorithms, won him the ACM Doctoral Dissertation Award in 1991. [1] Together with Yoav Freund, he invented the AdaBoost algorithm in 1996. They both received the Gödel prize in 2003 for this work.
Schapire was elected an AAAI Fellow in 2009. [2] In 2014, he was elected a member of the National Academy of Engineering for his contributions to machine learning through the invention and development of boosting algorithms. [3] In 2016, he was elected to the National Academy of Sciences. [4]
His son, Zachary Schapire, recently graduated from his alma mater, Brown University. His daughter, Jeni Schapire, is a singer-songwriter in Nashville, TN and a graduate of Oberlin College.
Ronald Linn Rivest is a cryptographer and computer scientist whose work has spanned the fields of algorithms and combinatorics, cryptography, machine learning, and election integrity. He is an Institute Professor at the Massachusetts Institute of Technology (MIT), and a member of MIT's Department of Electrical Engineering and Computer Science and its Computer Science and Artificial Intelligence Laboratory.
In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant : "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification. In contrast, a strong learner is a classifier that is arbitrarily well-correlated with the true classification.
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