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Research questionHow can ensemble classifiers retain accuracy and generalization with far fewer base learners?Adding base classifiers can improve an ensemble but increases computational cost. With only a few learners, differences in their class-specific strengths and weaknesses can reduce both accuracy and generalization.
Machine Learning
Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Achieving More with Less: A Tensor-Optimization-Powered Ensemble MethodThe source studies a multiclass ensemble that models each base classifier’s confidence across predicted and true classes, then uses a smooth, partially convex margin-based objective for optimization. It also establishes a zero-sum gradient property for the constrained optimization problem; the supplied evidence does not specify particular datasets or deployment settings.research paper · Sep 2, 2026
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