Learning to model sequences generated by switching distributions

Yoav Freund, Dana Ron

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

We study efficient algorithms for solving the following problem, which we call the switching distributions learning problem. A sequence S = ω1ω2...ωn, over a finite alphabet Σ is generated in the following way. The sequence is a concatenation of K runs, each of which is a consecutive subsequence. Each run is generated by independent random draws from a distribution pi over Σ, where pi is an element in a set of distributions {P1,...,PN}-The learning algorithm is given this sequence and its goal is to find approximations of the distributions p1,...,pN, and give an approximate segmentation of the sequence into its constituting runs. We give an efficient algorithm for solving this problem and show conditions under which the algorithm is guaranteed to work with high probability.

Original languageEnglish
Title of host publicationProceedings of the 8th Annual Conference on Computational Learning Theory, COLT 1995
PublisherAssociation for Computing Machinery, Inc
Pages41-50
Number of pages10
ISBN (Electronic)0897917235, 9780897917230
DOIs
StatePublished - 5 Jul 1995
Externally publishedYes
Event8th Annual Conference on Computational Learning Theory, COLT 1995 - Santa Cruz, United States
Duration: 5 Jul 19958 Jul 1995

Publication series

NameProceedings of the 8th Annual Conference on Computational Learning Theory, COLT 1995
Volume1995-January

Conference

Conference8th Annual Conference on Computational Learning Theory, COLT 1995
Country/TerritoryUnited States
CitySanta Cruz
Period5/07/958/07/95

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