A family of online boosting algorithms

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Standard

A family of online boosting algorithms. / Babenko, Boris; Yang, Ming Hsuan; Belongie, Serge.

I: 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009, 2009, s. 1346-1353.

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt

Harvard

Babenko, B, Yang, MH & Belongie, S 2009, 'A family of online boosting algorithms', 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009, s. 1346-1353. https://doi.org/10.1109/ICCVW.2009.5457453

APA

Babenko, B., Yang, M. H., & Belongie, S. (2009). A family of online boosting algorithms. 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009, 1346-1353. https://doi.org/10.1109/ICCVW.2009.5457453

Vancouver

Babenko B, Yang MH, Belongie S. A family of online boosting algorithms. 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009. 2009;1346-1353. https://doi.org/10.1109/ICCVW.2009.5457453

Author

Babenko, Boris ; Yang, Ming Hsuan ; Belongie, Serge. / A family of online boosting algorithms. I: 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009. 2009 ; s. 1346-1353.

Bibtex

@inproceedings{f15a725c96f443a28e7e9a8f49b98c8c,
title = "A family of online boosting algorithms",
abstract = "Boosting has become a powerful and useful tool in the machine learning and computer vision communities in recent years, and many interesting boosting algorithms have been developed to solve various challenging problems. In particular, Friedman proposed a flexible framework called gradient boosting, which has been used to derive boosting procedures for regression, multiple instance learning, semi-supervised learning, etc. Recently some attention has been given to online boosting (where the examples become available one at a time). In this paper we develop a boosting framework that can be used to derive online boosting algorithms for various cost functions. Within this framework, we derive online boosting algorithms for Logistic Regression, Least Squares Regression, and Multiple Instance Learning. We present promising results on a wide range of data sets.",
author = "Boris Babenko and Yang, {Ming Hsuan} and Serge Belongie",
year = "2009",
doi = "10.1109/ICCVW.2009.5457453",
language = "English",
pages = "1346--1353",
journal = "2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009",
note = "2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009 ; Conference date: 27-09-2009 Through 04-10-2009",

}

RIS

TY - GEN

T1 - A family of online boosting algorithms

AU - Babenko, Boris

AU - Yang, Ming Hsuan

AU - Belongie, Serge

PY - 2009

Y1 - 2009

N2 - Boosting has become a powerful and useful tool in the machine learning and computer vision communities in recent years, and many interesting boosting algorithms have been developed to solve various challenging problems. In particular, Friedman proposed a flexible framework called gradient boosting, which has been used to derive boosting procedures for regression, multiple instance learning, semi-supervised learning, etc. Recently some attention has been given to online boosting (where the examples become available one at a time). In this paper we develop a boosting framework that can be used to derive online boosting algorithms for various cost functions. Within this framework, we derive online boosting algorithms for Logistic Regression, Least Squares Regression, and Multiple Instance Learning. We present promising results on a wide range of data sets.

AB - Boosting has become a powerful and useful tool in the machine learning and computer vision communities in recent years, and many interesting boosting algorithms have been developed to solve various challenging problems. In particular, Friedman proposed a flexible framework called gradient boosting, which has been used to derive boosting procedures for regression, multiple instance learning, semi-supervised learning, etc. Recently some attention has been given to online boosting (where the examples become available one at a time). In this paper we develop a boosting framework that can be used to derive online boosting algorithms for various cost functions. Within this framework, we derive online boosting algorithms for Logistic Regression, Least Squares Regression, and Multiple Instance Learning. We present promising results on a wide range of data sets.

UR - http://www.scopus.com/inward/record.url?scp=77953195665&partnerID=8YFLogxK

U2 - 10.1109/ICCVW.2009.5457453

DO - 10.1109/ICCVW.2009.5457453

M3 - Conference article

AN - SCOPUS:77953195665

SP - 1346

EP - 1353

JO - 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

JF - 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

T2 - 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

Y2 - 27 September 2009 through 4 October 2009

ER -

ID: 302048989