Active Bibliography

1 ARCING CLASSIFIERS – Leo Breiman
277 Arcing Classifiers – Leo Breiman - 1998
Constructing discriminative biorthogonal bases for classification – Wit Jakuczun - 2004
Progress Report: Ensemble Methods in Connection with Neural Networks – Jakob Vogdrup Hansen, Ny Munkegade
On The Size of Training Set and – The Benefit From, Zhi-hua Zhou, Dan Wei, Gang Li, Honghua Dai - 2004
3 Selectively Ensembling Neural Classifiers – Z.-H. Zhou, J. Wu, W. Tang, Z.-Q. Chen, Zhi-hua Zhou, Jianxin Wu, Wei Tang, Zhao-qian Chen - 2002
3 Classifying Unseen Cases with Many Missing Values – Zijian Zheng, Boon Toh Low - 1999
1 On Weak Base Learners for Boosting Regression and Classification – Wenxin Jiang, Wenxin Jiang - 2000
3 Large Time Behavior Of Boosting Algorithms For Regression And Classification – Wenxin Jiang, W. Jiang - 1999
2 A Computational Environment for Extracting Rules from Databases – J. A. Baranauskas, M. C. Monard, G. E. A. P. A. Batista, Paulo So Carlos - 2000
1 Dynamic Coefficients in Neural Network Regression Ensembles – Jakob Vogdrup Hansen, Ny Munkegade - 1998
4 Some Results on Weakly Accurate Base Learners for Boosting Regression and Classification – Wenxin Jiang - 2000
TOPICS IN REGULARIZATION AND BOOSTING – Jerome Friedman, Trevor Hastie, Robert Tibshirani
Parallel Implementation and Investigation of Ensemble KNN – Firat Tekiner, Mike Pettipher, Ian Whittley, Tony Bagnall
2 and F.Tekiner, Attribute Selection Methods for Filtered Attribute Subspace based Bagging with Injected Randomness (FASBIR – I. M. Whittley, A. J. Bagnall, L. Bull, M. Pettipher, M. Studley
Parallel Data Mining- Case Study – Firat Tekiner, Mike Pettipher, Larry Bull, Mathew Studley, Ian Whittley, Tony Bagnall
7 A Method for Controlling Errors in Two-Class Classification – G. Felici, F. -s. Sun, K. Truemper, K. Truemper - 1998
1 Integrating Boosting and Stochastic Attribute Selection Committees for Further Improving the Performance of Decision Tree Learning – Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting - 1998
1 A Fast Scheme for Feature Subset Selection to Avoid Overfitting in – Luigi Rosa