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» Some Theory for Generalized Boosting Algorithms
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ECAI
2006
Springer
14 years 2 months ago
A Real Generalization of Discrete AdaBoost
Scaling discrete AdaBoost to handle real-valued weak hypotheses has often been done under the auspices of convex optimization, but little is generally known from the original boost...
Richard Nock, Frank Nielsen
ALT
2006
Springer
14 years 7 months ago
Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice
Abstract. We propose a thresholded ensemble model for ordinal regression problems. The model consists of a weighted ensemble of confidence functions and an ordered vector of thres...
Hsuan-Tien Lin, Ling Li
CSB
2004
IEEE
126views Bioinformatics» more  CSB 2004»
14 years 2 months ago
Boosted PRIM with Application to Searching for Oncogenic Pathway of Lung Cancer
Boosted PRIM (Patient Rule Induction Method) is a new algorithm developed for two-class classification problems. PRIM is a variation of those Tree-Based methods ( [4] Ch9.3), seek...
Pei Wang, Young Kim, Jonathan R. Pollack, Robert T...
COLT
2008
Springer
14 years 16 days ago
On the Margin Explanation of Boosting Algorithms
Much attention has been paid to the theoretical explanation of the empirical success of AdaBoost. The most influential work is the margin theory, which is essentially an upper bou...
Liwei Wang, Masashi Sugiyama, Cheng Yang, Zhi-Hua ...
ICML
2004
IEEE
14 years 11 months ago
Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model
Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. There are...
Zhihua Zhang, James T. Kwok, Dit-Yan Yeung