2026-09-21T15:33:58Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/81982 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Made available in DSpace on 2015-09-25T20:21:17Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 9971227.pdf: 9034020 bytes, checksum: 1fc694284ceaaa444bebb4b3c9423704 (MD5) Previous issue date: 2000 Ahuja, Narendra Yang, Ming-Hsuan 2015-09-25T20:21:17Z 2015-09-25T20:21:17Z 10000-01-01 2000 2000 Recently, Support Vector Machines (SVMs) have shown great potential in visual learning and pattern recognition problems. However, training a SVM for a large-scale problem is challenging since it is computationally intensive and the memory requirement grows with square of the number of training vectors. In the fourth part of this thesis, we have developed a geometric approach to train SVMs and compared its performance against conventional methods. Embargo set by: Seth Robbins for item 83263 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 167 p. Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2000. http://hdl.handle.net/2142/81982 (MiAaPQ)AAI9971227 eng Computer Science Hand Gesture Recognition and Face Detection in Images text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.