Guest Editors' Introduction
A Principal Components Approach to Combining Regression Estimates
Using Correspondence Analysis to Combine Classifiers
Linearly Combining Density Estimators via Stacking
Pasting Small Votes for Classification in Large Databases and On-Line
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
Structural Results About On-line Learning Models With and Without Queries
An Efficient Extension to Mixture Techniques for Prediction and Decision Trees
General and Efficient Multisplitting of Numerical Attributes