### Abstract

We introduce and analyze a new algorithm for linear classification which combines Rosenblatt's perceptron algorithm with Helmbold and Warmuth's leave-one-out method. Like Vapnik's maximal-margin classifier, our algorithm takes advantage of data that are linearly separable with large margins. Compared to Vapnik's algorithm, however, ours is much simpler to implement, and much more efficient in terms of computation time. We also show that our algorithm can be efficiently used in very high dimensional spaces using kernel functions. We performed some experiments using our algorithm, and some variants of it, for classifying images of handwritten digits. The performance of our algorithm is close to, but not as good as, the performance of maximal-margin classifiers on the same problem, while saving significantly on computation time and programming effort.

Original language | English (US) |
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Pages (from-to) | 277-296 |

Number of pages | 20 |

Journal | Machine Learning |

Volume | 37 |

Issue number | 3 |

DOIs | |

State | Published - Dec 1 1999 |

Externally published | Yes |

Event | Proceedings of the 1998 11th Annual Conference on 'Computational Learning Theory' (COLT98) - Madison, WI, USA Duration: Jul 24 1998 → Jul 26 1998 |

### All Science Journal Classification (ASJC) codes

- Software
- Artificial Intelligence

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## Cite this

*Machine Learning*,

*37*(3), 277-296. https://doi.org/10.1023/A:1007662407062