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

Kosuke Ishibashi, Kohei Hatano, Masayuki Takeda

研究成果: Contribution to conferencePaper査読

9 被引用数 (Scopus)

抄録

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.

本文言語英語
ページ69-80
ページ数12
出版ステータス出版済み - 12 1 2008
イベント21st Annual Conference on Learning Theory, COLT 2008 - Helsinki, フィンランド
継続期間: 7 9 20087 12 2008

その他

その他21st Annual Conference on Learning Theory, COLT 2008
国/地域フィンランド
CityHelsinki
Period7/9/087/12/08

All Science Journal Classification (ASJC) codes

  • 教育

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