{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/81730"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/81730","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Algorithms and Analysis for Multi-Category Classification","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. 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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.","Made available in DSpace on 2015-09-25T20:20:11Z (GMT). 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