Sparse weighted voting classifier selection and its linear programming relaxations

DOI10.1016/j.ipl.2012.03.004zbMath1243.68239OpenAlexW2126247482MaRDI QIDQ436579

Jonathan Eckstein, Noam Goldberg

Publication date: 25 July 2012

Published in: Information Processing Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ipl.2012.03.004

zbMATH Keywords

computational complexitymachine learningsparsityhardness of approximationintegrality gapweighted voting classification

Mathematics Subject Classification ID

Applications of mathematical programming (90C90) Mixed integer programming (90C11) Learning and adaptive systems in artificial intelligence (68T05) Linear programming (90C05) Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.) (68Q17)

Related Items (3)

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Uses Software

• AdaBoost.MH

Cites Work

• On the hardness of approximating label-cover
• On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
• The hardness of approximate optima in lattices, codes, and systems of linear equations
• Anti-Hadamard matrices, coin weighing, threshold gates, and indecomposable hypergraphs
• A decision-theoretic generalization of on-line learning and an application to boosting
• Robust trainability of single neurons
• Theory of majority decision elements
• On the Size of Weights for Threshold Gates
• Feature Selection via Mathematical Programming
• 10.1162/153244303322753751
• The Hadamard Maximum Determinant Problem
• Linear programming boosting via column generation

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