2026-09-21T02:25:48Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/81661 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2005. Chang, Kevin Chen-Chuan Hwang, Seung-won 2015-09-25T20:19:53Z 2015-09-25T20:19:53Z 10000-01-01 2005 2005 While building this system, we also observe that ranking functions learned often turn out to be quite complicated. Meanwhile, rank processing techniques have been focusing on supporting monotonic functions. We address this new challenge by developing a fresh new perspective abstracting query answering as an optimization problem, which enables to extend rank processing for any arbitrary functions. Made available in DSpace on 2015-09-25T20:19:53Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3182284.pdf: 4971175 bytes, checksum: 476a3a8934c5d7741aefb2e338862a74 (MD5) Previous issue date: 2005 Embargo set by: Seth Robbins for item 82942 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 137 p. http://hdl.handle.net/2142/81661 (MiAaPQ)AAI3182284 eng Computer Science Supporting Ranking for Data Retrieval text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.