A family of online boosting algorithms
Research output: Contribution to journal › Conference article › Research › peer-review
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.
Original language | English |
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Journal | 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009 |
Pages (from-to) | 1346-1353 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 2009 |
Externally published | Yes |
Event | 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009 - Kyoto, Japan Duration: 27 Sep 2009 → 4 Oct 2009 |
Conference
Conference | 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009 |
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Country | Japan |
City | Kyoto |
Period | 27/09/2009 → 04/10/2009 |
ID: 302048989