(Enter summary)
Abstract: Prior knowledge in the form of multiple polyhedral sets, each belonging to one of two categories, is introduced into a reformulation of a linear support vector machine classifier. The resulting formulation leads to a linear program that can be solved efficiently. Real world examples, from DNA sequencing and breast cancer prognosis, demonstrate the effectiveness of the proposed method. Numerical results show improvement in test set accuracy after the incorporation of prior knowledge into... (Update)
Context of citations to this paper: More
...resulting formulation leads to a linear program that can be solved e#ciently. This extends, in a rather unobvious fashion, previous work [3] that incorporated similar prior knowledge into a linear SVM classifier. Numerical tests on standard type test problems, such as exclusive...
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BibTeX entry: (Update)
G. Fung, O. L. Mangasarian, and J. Shavlik. Knowledge-based support vector machine classifiers. Technical Report 01-09, Data Mining Institute, Computer Sciences Department, University of Wisconsin, Madison, Wisconsin, November 2001. ftp://ftp.cs.wisc.edu/pub/dmi/tech-reports/01-09.ps, NIPS 2002 Proceedings, to http://citeseer.ist.psu.edu/fung02knowledgebased.html More
@misc{ fung01knowledgebased,
author = "G. Fung and O. Mangasarian and J. Shavlik",
title = "Knowledge-based support vector machine classifiers",
text = "G. Fung, O. L. Mangasarian, and J. Shavlik. Knowledge-based support vector
machine classifiers. Technical Report 01-09, Data Mining Institute, Computer
Sciences Department, University of Wisconsin, Madison, Wisconsin, November
2001. ftp://ftp.cs.wisc.edu/pub/dmi/tech-reports/01-09.ps, NIPS 2002 Proceedings,
to",
year = "2001",
url = "citeseer.ist.psu.edu/fung02knowledgebased.html" }
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