Near Linear Time Approximation Schemes for Clustering in Doubling Metrics
Offered By: IEEE via YouTube
Course Description
Overview
Explore a cutting-edge algorithm for efficient clustering in doubling metrics presented in this 19-minute IEEE conference talk. Delve into the innovative approach developed by Vincent Cohen-Addad, Andreas Emil Feldmann, and David Saulpic that achieves near-linear time approximation schemes for clustering problems. Gain insights into the theoretical foundations and practical implications of this breakthrough in computational geometry and machine learning.
Syllabus
Near Linear Time Approximation Schemes for Clustering in Doubling Metrics
Taught by
IEEE FOCS: Foundations of Computer Science
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