On coresets for support vector machines

DOI10.1016/j.tcs.2021.09.008OpenAlexW3197431719MaRDI QIDQ5919115

Murad Tukan, Dan Feldman, Daniela Rus, Cenk Baykal

Publication date: 21 October 2021

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2002.06469

zbMATH Keywords

data reductionsupport vector machineslarge-scale learningstreamingcoresets

Mathematics Subject Classification ID

Theory of computing (68Qxx)

Cites Work

• Pegasos: primal estimated sub-gradient solver for SVM
• Complete statistical theory of learning
• Deterministic coresets for stochastic matrices with applications to scalable sparse PageRank
• Dimensionality Reduction for k-Means Clustering and Low Rank Approximation
• On coresets for k-means and k-median clustering
• Weighted SGD for ℓ_p Regression with Randomized Preconditioning
• Performance of Johnson-Lindenstrauss transform for k -means and k -medians clustering
• Coresets for polytope distance
• Sublinear optimization for machine learning
• A unified framework for approximating and clustering data
• Improved bounds on the sample complexity of learning
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