### Abstract

We present a generalization of the Perceptron algorithm. The new algorithm performs a Perceptron-sty le update whenever the margin of an example is smaller than a predefined value. We derive worst case mistake bounds for our algorithm. As a byproduct we obtain a new mistake bound for the Perceptron algorithm in the inseparable case. We describe a multiclass extension of the algorithm. This extension is used in an experimental evaluation in which we compare the proposed algorithm to the Perceptron algorithm.

Original language | English (US) |
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Title of host publication | Learning Theory - 18th Annual Conference on Learning Theory, COLT 2005, Proceedings |

Publisher | Springer Verlag |

Pages | 264-278 |

Number of pages | 15 |

ISBN (Print) | 3540265562, 9783540265566 |

DOIs | |

State | Published - Jan 1 2005 |

Event | 18th Annual Conference on Learning Theory, COLT 2005 - Learning Theory - Bertinoro, Italy Duration: Jun 27 2005 → Jun 30 2005 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 3559 LNAI |

ISSN (Print) | 0302-9743 |

ISSN (Electronic) | 1611-3349 |

### Other

Other | 18th Annual Conference on Learning Theory, COLT 2005 - Learning Theory |
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Country | Italy |

City | Bertinoro |

Period | 6/27/05 → 6/30/05 |

### All Science Journal Classification (ASJC) codes

- Theoretical Computer Science
- Computer Science(all)

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## Cite this

Shalev-Shwartz, S., & Singer, Y. (2005). A new perspective on an old perceptron algorithm. In

*Learning Theory - 18th Annual Conference on Learning Theory, COLT 2005, Proceedings*(pp. 264-278). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3559 LNAI). Springer Verlag. https://doi.org/10.1007/11503415_18