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Abstract: We describe a new incremental algorithm for training linear threshold functions: the Relaxed Online Maximum Margin Algorithm, or ROMMA. ROMMA can be viewed as an approximation to the algorithm that repeatedly chooses the hyperplane that classifies previously seen examples correctly with the maximum margin. It is known that such a maximum-margin hypothesis can be computed by minimizing the length of the weight vector subject to a number of linear constraints. ROMMA works by maintaining a... (Update)
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BibTeX entry: (Update)
Yi Li and Phil M. Long. The relaxed online maximum margin algorithm. In Advances in Neural Information Processing Systems 13, 1999. http://citeseer.ist.psu.edu/li00relaxed.html More
@article{ li02relaxed,
author = "Yi Li and Philip M. Long",
title = "The Relaxed Online Maximum Margin Algorithm",
journal = "Machine Learning",
volume = "46",
number = "1--3",
publisher = "Kluwer Academic Publishers, Boston",
pages = "361--387",
year = "2002",
url = "citeseer.ist.psu.edu/li00relaxed.html" }
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