EBBS augments the MIO best-subsets objective with an aggregated expert prior expressed as a log-odds penalty so that selected features align with domain consensus while reducing to ordinary best subsets when experts provide no input.
Medoid Splits for Efficient Random Forests in Metric Spaces
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SmartIterator supplies method-specific workflows and coordinated visualizations to systematically supervise and interpret parameter sweeps of unsupervised data grouping techniques.
A Fréchet-based random-effects algorithm with M-estimation consistency guarantees is proposed for modeling non-Euclidean random objects in general metric spaces.
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Random-Effects Algorithm for Random Objects in Metric Spaces
A Fréchet-based random-effects algorithm with M-estimation consistency guarantees is proposed for modeling non-Euclidean random objects in general metric spaces.