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Title:Algorithms and Analysis for Multi-Category Classification
Author(s):Zimak, Dav Arthur
Doctoral Committee Chair(s):Roth, Dan
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Artificial Intelligence
Abstract:Third, we address an important algorithm in machine learning, the maximum margin classifier. Even with a conceptual understanding of how to extend maximum margin algorithms to more complex settings and performance guarantees of large margin classifiers, complex outputs render traditional approaches intractable in more complex settings. We introduce a new algorithm for learning maximum margin classifiers using coresets to find provably approximate solution to maximum margin linear separating hyperplane. Then, using the constraint classification framework, this algorithm applies directly to all of the previously mentioned complex-output domains. In addition, coresets motivate approximate algorithms for active learning and learning in the presence of outlier noise, where we give simple, elegant, and previously unknown proofs of their effectiveness.
Issue Date:2006
Description:120 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.
Other Identifier(s):(MiAaPQ)AAI3223769
Date Available in IDEALS:2015-09-25
Date Deposited:2006

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