### Abstract

A novel sequential minimal optimization (SMO) algorithm for support vector regression is proposed. This algorithm is based on Flake and Lawrence's SMO in which convex optimization problems with l variables are solved instead of standard quadratic programming problems with 2l variables where l is the number of training samples, but the strategy for working set selection is quite different. Experimental results show that the proposed algorithm is much faster than Flake and Lawrence's SMO and comparable to the fastest conventional SMO.

Original language | English |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Publisher | Springer Verlag |

Pages | 827-836 |

Number of pages | 10 |

Volume | 4232 LNCS |

ISBN (Print) | 3540464794, 9783540464792 |

Publication status | Published - 2006 |

Externally published | Yes |

Event | 13th International Conference on Neural Information Processing, ICONIP 2006 - Hong Kong, China Duration: Oct 3 2006 → Oct 6 2006 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
---|---|

Volume | 4232 LNCS |

ISSN (Print) | 03029743 |

ISSN (Electronic) | 16113349 |

### Other

Other | 13th International Conference on Neural Information Processing, ICONIP 2006 |
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Country | China |

City | Hong Kong |

Period | 10/3/06 → 10/6/06 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Science(all)
- Theoretical Computer Science

### Cite this

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*(Vol. 4232 LNCS, pp. 827-836). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4232 LNCS). Springer Verlag.

**A novel sequential minimal optimization algorithm for support vector regression.** / Guo, Jun; Takahashi, Norikazu; Nishi, Tetsuo.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).*vol. 4232 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4232 LNCS, Springer Verlag, pp. 827-836, 13th International Conference on Neural Information Processing, ICONIP 2006, Hong Kong, China, 10/3/06.

}

TY - GEN

T1 - A novel sequential minimal optimization algorithm for support vector regression

AU - Guo, Jun

AU - Takahashi, Norikazu

AU - Nishi, Tetsuo

PY - 2006

Y1 - 2006

N2 - A novel sequential minimal optimization (SMO) algorithm for support vector regression is proposed. This algorithm is based on Flake and Lawrence's SMO in which convex optimization problems with l variables are solved instead of standard quadratic programming problems with 2l variables where l is the number of training samples, but the strategy for working set selection is quite different. Experimental results show that the proposed algorithm is much faster than Flake and Lawrence's SMO and comparable to the fastest conventional SMO.

AB - A novel sequential minimal optimization (SMO) algorithm for support vector regression is proposed. This algorithm is based on Flake and Lawrence's SMO in which convex optimization problems with l variables are solved instead of standard quadratic programming problems with 2l variables where l is the number of training samples, but the strategy for working set selection is quite different. Experimental results show that the proposed algorithm is much faster than Flake and Lawrence's SMO and comparable to the fastest conventional SMO.

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UR - http://www.scopus.com/inward/citedby.url?scp=33750600332&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:33750600332

SN - 3540464794

SN - 9783540464792

VL - 4232 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 827

EP - 836

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -