Learning convex polytopes with margin

Lee Ad Gottlieb, Eran Kaufman, Gabriel Nivasch, Aryeh Kontorovich

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

We present an improved algorithm for properly learning convex polytopes in the realizable PAC setting from data with a margin. Our learning algorithm constructs a consistent polytope as an intersection of about t log t halfspaces with margins in time polynomial in t (where t is the number of halfspaces forming an optimal polytope). We also identify distinct generalizations of the notion of margin from hyperplanes to polytopes and investigate how they relate geometrically; this result may be of interest beyond the learning setting.

Original languageEnglish
Pages (from-to)5706-5716
Number of pages11
JournalAdvances in Neural Information Processing Systems
Volume2018-December
StatePublished - 1 Jan 2018
Event32nd Conference on Neural Information Processing Systems, NeurIPS 2018 - Montreal, Canada
Duration: 2 Dec 20188 Dec 2018

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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