1 citations · 1 across the 3 of their papers we have counts for
3 papers
Margin Maximization as Lossless Maximal Compression
Nikolaos Nikolaou, Henry Reeve, Gavin Brown
The ultimate goal of a supervised learning algorithm is to produce models constructed on the training data that can generalize well to new examples. In classification, functional m…
Better Boosting with Bandits for Online Learning
Nikolaos Nikolaou, Joseph Mellor, Nikunj C. Oza +1
Probability estimates generated by boosting ensembles are poorly calibrated because of the margin maximization nature of the algorithm. The outputs of the ensemble need to be prope…
Ranking Biomarkers Through Mutual Information
Konstantinos Sechidis, Emily Turner, Paul D. Metcalfe +2
We study information theoretic methods for ranking biomarkers. In clinical trials there are two, closely related, types of biomarkers: predictive and prognostic, and disentangling…