Online learning of approximate maximum p-norm margin classifiers with bias

Kosuke Ishibashi, Kohei Hatano, Masayuki Takeda

Research output: Contribution to conferencePaperpeer-review

10 Citations (Scopus)

Abstract

We propose a new online learning algorithm which provably approximates maximum margin classifiers with bias, where the margin is defined in terms of p-norm distance. Although learning of linear classifiers with bias can be reduced to learning of those without bias, the known reduction might lose the margin and slow down the convergence of online learning algorithms. Our algorithm, unlike previous online learning algorithms, implicitly uses a new reduction which preserves the margin and avoids such possible deficiencies. Our preliminary experiments show that our algorithm runs much faster than previous algorithms especially when the underlying linear classifier has large bias.

Original languageEnglish
Pages69-80
Number of pages12
Publication statusPublished - Dec 1 2008
Event21st Annual Conference on Learning Theory, COLT 2008 - Helsinki, Finland
Duration: Jul 9 2008Jul 12 2008

Other

Other21st Annual Conference on Learning Theory, COLT 2008
CountryFinland
CityHelsinki
Period7/9/087/12/08

All Science Journal Classification (ASJC) codes

  • Education

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