Modeling text with generalizable Gaussian mixtures

Research output: Research - peer-reviewArticle in proceedings – Annual report year: 2000

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We apply and discuss generalizable Gaussian mixture (GGM) models for text mining. The model automatically adapts model complexity for a given text representation. We show that the generalizability of these models depends on the dimensionality of the representation and the sample size. We discuss the relation between supervised and unsupervised learning in the test data. Finally, we implement a novelty detector based on the density model.
Original languageEnglish
Title of host publicationIEEE Procedings of Acoustics, Speech, and Signal Processing
Volume6
Place of PublicationIstanbul, Turkey
PublisherIEEE
Publication date2000
Pages3494-3497
ISBN (Print)0-7803-6293-4
DOIs
StatePublished - 2000
EventIEEE International Conference on Acoustics, Speech, and Signal Processing 1995 - Detroit, MI, United States
Duration: 9 May 199512 May 1995

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing 1995
CountryUnited States
CityDetroit, MI
Period09/05/199512/05/1995

Bibliographical note

Copyright: 2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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