Combines D-optimality and diversity-maximizing selection in an epsilon-greedy loop to create compact training sets for sparse group additivity and kernel ridge regression models of molecular properties.
Active learning with support vector machine applied to gene expression data for cancer classification
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Designing compact training sets for data-driven molecular property prediction
Combines D-optimality and diversity-maximizing selection in an epsilon-greedy loop to create compact training sets for sparse group additivity and kernel ridge regression models of molecular properties.