Active Bibliography

14 Distributed Data Mining: Scaling up and beyond – Foster Provost - 1999
62 Tree induction vs. logistic regression: A learning-curve analysis – Claudia Perlich, Foster Provost, Jeffrey S. Simonoff - 2001
18 A Survey of Methods for Scaling Up Inductive Learning Algorithms – Foster J. Provost, Venkateswarlu Kolluri - 1997
7 Constructing New Attributes for Decision Tree Learning – Zijian Zheng - 1996
5 Modelling Classification Performance for Large Data Sets - An Empirical Study – Baohua Gu, Feifang Hu, Huan Liu - 2001
6 A Comparative Evaluation of Meta-Learning Strategies over Large and Distributed Data Sets – Andreas L. Prodromidis, Salvatore J. Stolfo - 1999
i To Elham ii Acknowledgements – Winton H. E. Davies, Winton H. E. Davies - 2001
13 Pruning decision trees and lists – Eibe Frank - 2000
44 An Extensible Meta-Learning Approach for Scalable and Accurate Inductive Learning – Philip Kin-Wah Chan - 1996
2 SAMPLE SIZE AND MODELING ACCURACY WITH DECISION-TREE BASED DATA MINING TOOLS – James Morgan, Robert Dougherty, Allan Hilchie, Bern Carey
41 Inductive Policy: The Pragmatics of Bias Selection – Foster John Provost, Bruce G. Buchanan - 1995
A Study of Support Vectors on Model Independent Example Selection – Nadeem Ahmed, Syed Huan, Liu Kah, Kay Sung
2 Data reduction via adaptive sampling – Xiao-bai Li - 2002
288 Mining high-speed data streams – Pedro Domingos - 2000
16 On Issues of Instance Selection – Huan Liu, Hiroshi Motoda - 2002
5 More Efficient Windowing – Johannes F├╝rnkranz - 1997
1 The Effect of Numeric Features on the Scalability of Inductive Learning Programs – Georgios Paliouras, David S. Bree - 1995
RIDE: Rule-Learning in a Distributed Environment – Nitesh Chawla - 2000
6 Reduced-Error Pruning With Significance Tests – Eibe Frank, Ian H. Witten - 1998