2026-09-19T06:27:49Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/81730 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

2006 Roth, Dan Zimak, Dav Arthur 2015-09-25T20:20:11Z 2015-09-25T20:20:11Z 10000-01-01 2006 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. Made available in DSpace on 2015-09-25T20:20:11Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3223769.pdf: 3414416 bytes, checksum: b87276e4f1b0841d12273ecc51a8166d (MD5) Previous issue date: 2006 Embargo set by: Seth Robbins for item 83011 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs U of I Only 120 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. http://hdl.handle.net/2142/81730 (MiAaPQ)AAI3223769 eng Artificial Intelligence Algorithms and Analysis for Multi-Category Classification text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.