Join Our Newsletter

Free Online Productivity Tools
i2Speak
i2Symbol
i2OCR
iTex2Img
iWeb2Print
iWeb2Shot
i2Type
iPdf2Split
iPdf2Merge
i2Bopomofo
i2Pinyin
i2Cantonese
i2Cangjie
i2Arabic
i2Style
i2Image
i2PDF
iLatex2Rtf
Sci2ools

ALT

2008

Springer

2008

Springer

In this paper we discuss boosting algorithms that maximize the soft margin of the produced linear combination of base hypotheses. LPBoost is the most straightforward boosting algorithm for doing this. It maximizes the soft margin by solving a linear programming problem. While it performs well on natural data, there are cases where the number of iterations is linear in the number of examples instead of logarithmic. By simply adding a relative entropy regularization to the linear objective of LPBoost, we arrive at the Entropy Regularized LPBoost algorithm for which we prove a logarithmic iteration bound. A previous algorithm, called SoftBoost, has the same iteration bound, but the generalization error of this algorithm often decreases slowly in early iterations. Entropy Regularized LPBoost does not suﬀer from this problem and has a simpler, more natural motivation.

Related Content

Added |
14 Mar 2010 |

Updated |
14 Mar 2010 |

Type |
Conference |

Year |
2008 |

Where |
ALT |

Authors |
Manfred K. Warmuth, Karen A. Glocer, S. V. N. Vishwanathan |

Comments (0)