2026-09-18T19:35:58Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/81761 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Gerald DeJong Epshteyn, Arkady 2015-09-25T20:20:20Z 2015-09-25T20:20:20Z 10000-01-01 2007 2007 We explore this task in three contexts: classification (determining the subject of a newsgroup posting), control (learning to perform tasks such as driving a car up a mountain in simulation), and optimization (optimizing performance of linear algebra operations on different hardware platforms). For the text categorization problem, we introduce a novel algorithm which efficiently integrates prior knowledge into large margin classification. For reinforcement learning, we introduce a novel framework for defining and solving planning problems in terms of qualitative statements about the world. In compiler optimization, Bayesian prior based on an analytic model of hardware is combined with empirical measurements of performance of optimized code to determine the maximum-a-posteriori estimates of the optimization parameters. Made available in DSpace on 2015-09-25T20:20:20Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3269889.pdf: 3876494 bytes, checksum: f17461cb5d5dba04569aacb0a57920b1 (MD5) Previous issue date: 2007 Embargo set by: Seth Robbins for item 83042 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 114 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007. http://hdl.handle.net/2142/81761 (MiAaPQ)AAI3269889 eng Artificial Intelligence Combining Prior Knowledge and Data: Beyond the Bayesian Framework text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.