The nature of statistical learning theory
The nature of statistical learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Intelligent information query and browsing system using active learning for pattern classification
Intelligent information query and browsing system using active learning for pattern classification
Nonlinear Boosting Projections for Ensemble Construction
The Journal of Machine Learning Research
A SVM method for web page categorization based on weight adjustment and boosting mechanism
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
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Combining boosting and Support Vector Machine (SVM) is proved to be beneficial, but it is too complex to be feasible. This paper introduces an efficient way to boost SVM. It embraces the idea of active learning to dynamically select "important" samples into training sample set for constructing base classifiers. This method maintains a small training sample set with settled size in order to control the complexity of each base classifier. Other than construct each base SVM classifier directly, it uses the training samples only for finding support vectors. This way to combine boosting and SVM is proved to be accurate and efficient by experimental results.