2026-09-20T02:14:41Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/30953 2023-07-10 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Blahut, Richard E. Pierce, Austin 2012-05-22T00:18:12Z 2012-05-22T00:18:12Z 2012-05 2012-05-22T00:18:12Z 2012-05 A new method is proposed that exploits the underlying information theoretic structure in the input data to evaluate the ability of a kernel to successfully separate a class in some feature space. This method is built on the fundamental idea that kernel density estimation in some input space is equivalent to an inner product on some Hilbert space. Estimators of Renyi's generalized form of information theoretic measurements reduce to a form that gives an elegant characterization of the geometric properties of the kernel in the feature space. It is shown how these estimators can be used to evaluate the kernel of a support vector machine. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2012-04-21T15:37:52Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Pierce_Austin.pdf: 467162 bytes, checksum: a959652412330fdbdfeb891bceff48ae (MD5) Made available in DSpace on 2012-05-22T00:18:12Z (GMT). No. of bitstreams: 2 Pierce_Austin.pdf: 467162 bytes, checksum: a959652412330fdbdfeb891bceff48ae (MD5) license.txt: 4063 bytes, checksum: 6ff2117e7a65d42f2c7c81ca144c0a22 (MD5) http://hdl.handle.net/2142/30953 en Copyright 2012 Austin Pierce Renyi Support Vector Machine (SVM) Renyi Entropy Using information theoretic measures to evaluate support vector machine kernels text Electrical & Computer Eng 1933 Electrical & Computer Engr 1200 University of Illinois at Urbana-Champaign Thesis M.S. MS:Electr & Computer Eng-UIUC 10KS1200MS