Serrri-supervised marginboost

F. D'Alché-Buc; Yves Grandvalet; Christophe Ambroise

Serrri-supervised marginboost

F. D'Alché-Buc
, Yves Grandvalet
, Christophe Ambroise

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

Abstract

In many discrimination problems a largo amount of data is available but only a few of them are labeled. This provides a strong motivation to improve or develop methods for semi-supervised learning. In this paper, boosting is generalized to this task within the optimization framework of MarginBoost . We extend the margin definition to unlabeled data and develop the gradient descent algorithm that corresponds to the resulting margin cost function. This met a-learning scheme can be applied to any base classifier able to benefit from unlabeled data. We propose here to apply it to mixture models trained with an Expectation-Maximization algorithm. Promising results are presented on benchmarks with different rates of labeled data.

Original language	English
Title of host publication	Advances in Neural Information Processing Systems 14 - Proceedings of the 2001 Conference, NIPS 2001
Publisher	Neural information processing systems foundation
ISBN (Print)	0262042088, 9780262042086
Publication status	Published - 1 Jan 2002
Externally published	Yes
Event	15th Annual Neural Information Processing Systems Conference, NIPS 2001 - Vancouver, BC, Canada Duration: 3 Dec 2001 → 8 Dec 2001

Publication series

Name	Advances in Neural Information Processing Systems
ISSN (Print)	1049-5258

Conference

Conference	15th Annual Neural Information Processing Systems Conference, NIPS 2001
Country/Territory	Canada
City	Vancouver, BC
Period	3/12/01 → 8/12/01

Cite this

@inproceedings{7c808afe9584479b81d64f013b8fd2a2,

title = "Serrri-supervised marginboost",

abstract = "In many discrimination problems a largo amount of data is available but only a few of them are labeled. This provides a strong motivation to improve or develop methods for semi-supervised learning. In this paper, boosting is generalized to this task within the optimization framework of MarginBoost . We extend the margin definition to unlabeled data and develop the gradient descent algorithm that corresponds to the resulting margin cost function. This met a-learning scheme can be applied to any base classifier able to benefit from unlabeled data. We propose here to apply it to mixture models trained with an Expectation-Maximization algorithm. Promising results are presented on benchmarks with different rates of labeled data.",

author = "F. D'Alch{\'e}-Buc and Yves Grandvalet and Christophe Ambroise",

year = "2002",

month = jan,

day = "1",

language = "English",

isbn = "0262042088",

series = "Advances in Neural Information Processing Systems",

publisher = "Neural information processing systems foundation",

booktitle = "Advances in Neural Information Processing Systems 14 - Proceedings of the 2001 Conference, NIPS 2001",

note = "15th Annual Neural Information Processing Systems Conference, NIPS 2001 ; Conference date: 03-12-2001 Through 08-12-2001",

}

D'Alché-Buc, F, Grandvalet, Y & Ambroise, C 2002, Serrri-supervised marginboost. in Advances in Neural Information Processing Systems 14 - Proceedings of the 2001 Conference, NIPS 2001. Advances in Neural Information Processing Systems, Neural information processing systems foundation, 15th Annual Neural Information Processing Systems Conference, NIPS 2001, Vancouver, BC, Canada, 3/12/01.

Serrri-supervised marginboost. / D'Alché-Buc, F.; Grandvalet, Yves; Ambroise, Christophe.
Advances in Neural Information Processing Systems 14 - Proceedings of the 2001 Conference, NIPS 2001. Neural information processing systems foundation, 2002. (Advances in Neural Information Processing Systems).

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

TY - GEN

T1 - Serrri-supervised marginboost

AU - D'Alché-Buc, F.

AU - Grandvalet, Yves

AU - Ambroise, Christophe

PY - 2002/1/1

Y1 - 2002/1/1

N2 - In many discrimination problems a largo amount of data is available but only a few of them are labeled. This provides a strong motivation to improve or develop methods for semi-supervised learning. In this paper, boosting is generalized to this task within the optimization framework of MarginBoost . We extend the margin definition to unlabeled data and develop the gradient descent algorithm that corresponds to the resulting margin cost function. This met a-learning scheme can be applied to any base classifier able to benefit from unlabeled data. We propose here to apply it to mixture models trained with an Expectation-Maximization algorithm. Promising results are presented on benchmarks with different rates of labeled data.

AB - In many discrimination problems a largo amount of data is available but only a few of them are labeled. This provides a strong motivation to improve or develop methods for semi-supervised learning. In this paper, boosting is generalized to this task within the optimization framework of MarginBoost . We extend the margin definition to unlabeled data and develop the gradient descent algorithm that corresponds to the resulting margin cost function. This met a-learning scheme can be applied to any base classifier able to benefit from unlabeled data. We propose here to apply it to mixture models trained with an Expectation-Maximization algorithm. Promising results are presented on benchmarks with different rates of labeled data.

M3 - Conference contribution

AN - SCOPUS:84899006611

SN - 0262042088

SN - 9780262042086

T3 - Advances in Neural Information Processing Systems

BT - Advances in Neural Information Processing Systems 14 - Proceedings of the 2001 Conference, NIPS 2001

PB - Neural information processing systems foundation

T2 - 15th Annual Neural Information Processing Systems Conference, NIPS 2001

Y2 - 3 December 2001 through 8 December 2001

ER -

Serrri-supervised marginboost

Abstract

Publication series

Conference

Fingerprint

Cite this