Machine Learning
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Further results on the margin distribution
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Proceedings of the 1998 conference on Advances in neural information processing systems II
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Column Generation Algorithm For Boosting
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Modelling metabolic pathways using stochastic logic programs-based ensemble methods
CMSB'04 Proceedings of the 20 international conference on Computational Methods in Systems Biology
Margin distribution based bagging pruning
Neurocomputing
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The paper considers applying a boosting strategy to optimise the generalisation bound obtained recently by Shawe-Taylor and Cristianini [7] in terms of the two norm of the slack variables. The formulation performs gradient descent over the quadratic loss function which is insensitive to points with a large margin. A novel feature of this algorithm is a principled adaptation of the size of the target margin. Experiments with text and UCI data shows that the new algorithm improves the accuracy of boosting. DMarginBoost generally achieves significant improvements over Adaboost.