Selecting training inputs via greedy rank covering

Adam L. Buchsbaum, Jan P.H. Van Santen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

We present a general method for selecting a small set of training inputs, the observations of which will suffice to estimate the parameters of a given linear model. We exemplify the algorithm in terms of predicting segmental duration of phonetic-segment feature vectors in a text-to-speech synthesizer, but the algorithm will work for any linear model and its associated domain.

Original languageEnglish (US)
Title of host publicationProceedings of the 7th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 1996
PublisherAssociation for Computing Machinery
Pages288-295
Number of pages8
ISBN (Electronic)0898713668
StatePublished - Jan 28 1996
Event7th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 1996 - Atlanta, United States
Duration: Jan 28 1996Jan 30 1996

Publication series

NameProceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms
VolumePart F129447

Other

Other7th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 1996
Country/TerritoryUnited States
CityAtlanta
Period1/28/961/30/96

ASJC Scopus subject areas

  • Software
  • Mathematics(all)

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