A family of online boosting algorithms
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A family of online boosting algorithms. / Babenko, Boris; Yang, Ming Hsuan; Belongie, Serge.
In: 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009, 2009, p. 1346-1353.Research output: Contribution to journal › Conference article › Research › peer-review
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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