Committee polyhedral separability: complexity and polynomial approximation

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)
Original languageEnglish
Pages (from-to)231-251
Number of pages21
JournalMachine Learning
Volume101
Issue number1-3
DOIs
Publication statusPublished - Oct 2015

Keywords

  • Polyhedral separability
  • Affine committees
  • Computational complexity
  • Approximability
  • ALGORITHM
  • MAJORITY
  • NP

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

@article{c376f56d00c148028648d842a49ab51d,
title = "Committee polyhedral separability: complexity and polynomial approximation",
keywords = "Polyhedral separability, Affine committees, Computational complexity, Approximability, ALGORITHM, MAJORITY, NP",
author = "Michael Khachay",
year = "2015",
month = "10",
doi = "10.1007/s10994-015-5505-0",
language = "English",
volume = "101",
pages = "231--251",
journal = "Machine Learning",
issn = "0885-6125",
publisher = "Kluwer Academic Publishers",
number = "1-3",

}

Committee polyhedral separability: complexity and polynomial approximation. / Khachay, Michael.

In: Machine Learning, Vol. 101, No. 1-3, 10.2015, p. 231-251.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

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KW - Affine committees

KW - Computational complexity

KW - Approximability

KW - ALGORITHM

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KW - NP

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U2 - 10.1007/s10994-015-5505-0

DO - 10.1007/s10994-015-5505-0

M3 - Article

VL - 101

SP - 231

EP - 251

JO - Machine Learning

JF - Machine Learning

SN - 0885-6125

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