Learning to benchmark is coming at 04/27/2020 - 4:00pm KEC 1001 Mon, 04/27/2020 - 4:00pm
Alfred O. Hero III John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science, R. Jamison and Betty Williams Professor of Engineering, University of Michigan, Ann Arbor Abstract: We address the problem of learning an achievable lower bound on classification error from a labeled sample. We establish a framework for this meta-learning problem, which we call benchmark learning. Benchmark learning leads to an accurate data-driven predictor of performance of a Bayes optimal classifier without having to construct the classifier and without assuming any parametric model for the data. The resultant predictor can be used to establish whether it is possible to improve classification performance of a specific classifier. It also yields a stopping rule for sequentially trained classifiers. In addition, The talk will cover relevant background, theory, algorithms, and applications of benchmark learning. Bio: Read more: https://eecs.oregonstate.edu/colloquium/learning-benchmark [1] [1] https://eecs.oregonstate.edu/colloquium/learning-benchmark
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