{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/78437"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/78437","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Selective algorithms for large-scale classification and structured learning","abstract":"Made available in DSpace on 2015-07-22T22:17:14Z (GMT). No. of bitstreams: 2 CHANG-DISSERTATION-2015.pdf: 1718796 bytes, checksum: acb2f0fcac6237b94e5733d5534763cb (MD5) LICENSE.txt: 4210 bytes, checksum: 781f641005bc15b659d6f1cee238b5c2 (MD5) Previous issue date: 2015-04-23","abstract_html":"Made available in DSpace on 2015-07-22T22:17:14Z (GMT). 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Our results lead to efficient learning algorithms for large-scale binary classification models, structured prediction models and for online clustering models which, in turn, support reduction in problem size, improvements in training and evaluation speed and improved performance. We have used our algorithms to learn expressive models from large amounts of annotated data and achieve state-of-the art performance on several natural language processing tasks.","Submission original under an indefinite embargo labeled 'Open Access'. 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