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» Ensemble Methods in Machine Learning
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ICML
2001
IEEE
16 years 3 months ago
Constrained K-means Clustering with Background Knowledge
Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data in...
Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan ...
142
Voted
ICML
1999
IEEE
16 years 3 months ago
AdaCost: Misclassification Cost-Sensitive Boosting
AdaCost, a variant of AdaBoost, is a misclassification cost-sensitive boosting method. It uses the cost of misclassifications to update the training distribution on successive boo...
Wei Fan, Salvatore J. Stolfo, Junxin Zhang, Philip...
ICML
1996
IEEE
16 years 3 months ago
Toward Optimal Feature Selection
In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for de ning the theoretically optimal, but computationally intr...
Daphne Koller, Mehran Sahami
RECOMB
2006
Springer
16 years 2 months ago
Improving Prediction of Zinc Binding Sites by Modeling the Linkage Between Residues Close in Sequence
Abstract. We describe and empirically evaluate machine learning methods for the prediction of zinc binding sites from protein sequences. We start by observing that a data set consi...
Sauro Menchetti, Andrea Passerini, Paolo Frasconi,...
118
Voted
ICML
2009
IEEE
15 years 9 months ago
Split variational inference
We propose a deterministic method to evaluate the integral of a positive function based on soft-binning functions that smoothly cut the integral into smaller integrals that are ea...
Guillaume Bouchard, Onno Zoeter