2026-09-19T04:14:51Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/80908 2023-07-11 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Huang, Thomas S. Petrovic, Nemanja D. 2015-09-25T20:08:46Z 2015-09-25T20:08:46Z 10000-01-01 2005 2005 Having the applications as the ultimate goal, I will demonstrate the algorithmic techniques for speeding up the naive learning in the graphical models by orders of magnitude. In that sense, I will investigate signal processing techniques, approximate methods, and online learning. I will demonstrate how the theory and algorithms usefully apply to the variety of tasks ranging from video clustering and stabilization, to video retrieval and building of the similarity measures between the distributions. Made available in DSpace on 2015-09-25T20:08:46Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3182351.pdf: 4112701 bytes, checksum: 51e743673297bf8523d83280f7c1dca3 (MD5) Previous issue date: 2005 Embargo set by: Seth Robbins for item 82190 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 85 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2005. http://hdl.handle.net/2142/80908 (MiAaPQ)AAI3182351 eng Artificial Intelligence Graphical Models for Video Understanding text Electrical Engineering Electrical Engineering University of Illinois at Urbana-Champaign Dissertation Ph.D.