Hierarchical design of fast minimum disagreement algorithms

DOI10.1016/j.tcs.2017.11.022zbMath1388.68249OpenAlexW2772456629MaRDI QIDQ1704562

Malte Darnstädt, Christoph Ries, Hans Ulrich Simon

Publication date: 12 March 2018

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

Full work available at URL: https://doi.org/10.1016/j.tcs.2017.11.022

zbMATH Keywords

computational complexitylearning theorydata structureaxis-parallel rectangleminimum disagreement algorithm

Mathematics Subject Classification ID

Analysis of algorithms and problem complexity (68Q25) Learning and adaptive systems in artificial intelligence (68T05) Data structures (68P05)

Uses Software

• AdaBoost.MH

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Sharper bounds for Gaussian and empirical processes
• Toward efficient agnostic learning
• A decision-theoretic generalization of on-line learning and an application to boosting
• Very simple classification rules perform well on most commonly used datasets
• Maximum-weight planar boxes in \(O(n^2)\) time (and better)
• Bichromatic separability with two boxes: A general approach
• Computational limitations on learning from examples
• Understanding Machine Learning
• On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities

This page was built for publication: Hierarchical design of fast minimum disagreement algorithms