Multi-view hidden markov perceptrons

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Standard

Multi-view hidden markov perceptrons. / Brefeld, Ulf; Büscher, Christoph; Scheffer, Tobias.
Lernen, Wissensentdeckung und Adaptivitat, LWA 2005. ed. / Mathias Bauer; Boris Brandherm; Johannes Fürnkranz; Gunter Grieser; Andreas Hotho; Andreas Jedlitschka; Alexander Kröner. Saarbrücken: Gesellschaft für Informatik e.V., 2005. p. 134-138.

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Harvard

Brefeld, U, Büscher, C & Scheffer, T 2005, Multi-view hidden markov perceptrons. in M Bauer, B Brandherm, J Fürnkranz, G Grieser, A Hotho, A Jedlitschka & A Kröner (eds), Lernen, Wissensentdeckung und Adaptivitat, LWA 2005. Gesellschaft für Informatik e.V., Saarbrücken, pp. 134-138, Lernen, Wissensentdeckung und Adaptivitat, LWA 2005, Saarbrücken, Germany, 10.10.05.

APA

Brefeld, U., Büscher, C., & Scheffer, T. (2005). Multi-view hidden markov perceptrons. In M. Bauer, B. Brandherm, J. Fürnkranz, G. Grieser, A. Hotho, A. Jedlitschka, & A. Kröner (Eds.), Lernen, Wissensentdeckung und Adaptivitat, LWA 2005 (pp. 134-138). Gesellschaft für Informatik e.V..

Vancouver

Brefeld U, Büscher C, Scheffer T. Multi-view hidden markov perceptrons. In Bauer M, Brandherm B, Fürnkranz J, Grieser G, Hotho A, Jedlitschka A, Kröner A, editors, Lernen, Wissensentdeckung und Adaptivitat, LWA 2005. Saarbrücken: Gesellschaft für Informatik e.V. 2005. p. 134-138

Bibtex

@inbook{9aceb3683a9f4c16af9ba58f34202e56,
title = "Multi-view hidden markov perceptrons",
abstract = "Discriminative learning techniques for sequential data have proven to be more effective than generative models for named entity recognition, information extraction, and other tasks of discrimination. However, semi-supervised learning mechanisms that utilize inexpensive unlabeled sequences in addition to few labeled sequences – such as the Baum-Welch algorithm – are available only for generative models. The multi-view approach is based on the principle of maximizing the consensus among multiple independent hypotheses; we develop this principle into a semisupervised hidden Markov perceptron algorithm. Experiments reveal that the resulting procedure utilizes unlabeled data effectively and discriminates more accurately than its purely supervised counterparts.",
keywords = "Informatics, Business informatics",
author = "Ulf Brefeld and Christoph B{\"u}scher and Tobias Scheffer",
year = "2005",
language = "English",
pages = "134--138",
editor = "Mathias Bauer and Boris Brandherm and Johannes F{\"u}rnkranz and Gunter Grieser and Andreas Hotho and Andreas Jedlitschka and Alexander Kr{\"o}ner",
booktitle = "Lernen, Wissensentdeckung und Adaptivitat, LWA 2005",
publisher = "Gesellschaft f{\"u}r Informatik e.V.",
address = "Germany",
note = "Lernen, Wissensentdeckung und Adaptivitat, LWA 2005, LWA 2005 ; Conference date: 10-10-2005 Through 12-10-2005",
url = "http://www.dfki.de/lwa2005/",

}

RIS

TY - CHAP

T1 - Multi-view hidden markov perceptrons

AU - Brefeld, Ulf

AU - Büscher, Christoph

AU - Scheffer, Tobias

PY - 2005

Y1 - 2005

N2 - Discriminative learning techniques for sequential data have proven to be more effective than generative models for named entity recognition, information extraction, and other tasks of discrimination. However, semi-supervised learning mechanisms that utilize inexpensive unlabeled sequences in addition to few labeled sequences – such as the Baum-Welch algorithm – are available only for generative models. The multi-view approach is based on the principle of maximizing the consensus among multiple independent hypotheses; we develop this principle into a semisupervised hidden Markov perceptron algorithm. Experiments reveal that the resulting procedure utilizes unlabeled data effectively and discriminates more accurately than its purely supervised counterparts.

AB - Discriminative learning techniques for sequential data have proven to be more effective than generative models for named entity recognition, information extraction, and other tasks of discrimination. However, semi-supervised learning mechanisms that utilize inexpensive unlabeled sequences in addition to few labeled sequences – such as the Baum-Welch algorithm – are available only for generative models. The multi-view approach is based on the principle of maximizing the consensus among multiple independent hypotheses; we develop this principle into a semisupervised hidden Markov perceptron algorithm. Experiments reveal that the resulting procedure utilizes unlabeled data effectively and discriminates more accurately than its purely supervised counterparts.

KW - Informatics

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=84874002288&partnerID=8YFLogxK

M3 - Article in conference proceedings

AN - SCOPUS:84874002288

SP - 134

EP - 138

BT - Lernen, Wissensentdeckung und Adaptivitat, LWA 2005

A2 - Bauer, Mathias

A2 - Brandherm, Boris

A2 - Fürnkranz, Johannes

A2 - Grieser, Gunter

A2 - Hotho, Andreas

A2 - Jedlitschka, Andreas

A2 - Kröner, Alexander

PB - Gesellschaft für Informatik e.V.

CY - Saarbrücken

T2 - Lernen, Wissensentdeckung und Adaptivitat, LWA 2005

Y2 - 10 October 2005 through 12 October 2005

ER -

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