Multi-view discriminative sequential learning

Ulf Brefeld; Christoph Büscher; Tobias Scheffer

doi:10.1007/11564096_11

Multi-view discriminative sequential learning

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Standard

Multi-view discriminative sequential learning. / Brefeld, Ulf; Büscher, Christoph; Scheffer, Tobias.
Machine Learning: ECML 2005: 16th European Conference on Machine Learning. Springer, 2005. p. 60-71 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3720 LNAI).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Harvard

Brefeld, U, Büscher, C & Scheffer, T 2005, Multi-view discriminative sequential learning. in Machine Learning: ECML 2005: 16th European Conference on Machine Learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3720 LNAI, Springer, pp. 60-71, 16th European Conference on Machine Learning, Porto, Portugal, 03.10.05. https://doi.org/10.1007/11564096_11

APA

Brefeld, U., Büscher, C., & Scheffer, T. (2005). Multi-view discriminative sequential learning. In Machine Learning: ECML 2005: 16th European Conference on Machine Learning (pp. 60-71). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3720 LNAI). Springer. https://doi.org/10.1007/11564096_11

Vancouver

Brefeld U, Büscher C, Scheffer T. Multi-view discriminative sequential learning. In Machine Learning: ECML 2005: 16th European Conference on Machine Learning. Springer. 2005. p. 60-71. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11564096_11

Bibtex

@inbook{6fcea8700db74e138fa597ba9e24dd2d,

title = "Multi-view discriminative sequential learning",

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 semi-supervised hidden Markov perceptron, and a semi-supervised hidden Markov support vector learning algorithm. Experiments reveal that the resulting procedures utilize unlabeled data effectively and discriminate more accurately than their purely supervised counterparts.",

keywords = "Informatics, Unlabeled Data, Neural Information Processing System, Name Entity Recognition, Entity Recognition, Word Sense Disambiguation, Business informatics",

author = "Ulf Brefeld and Christoph B{\"u}scher and Tobias Scheffer",

note = "Funding Information: Thanks to NICT for their support, Takayuki Kuribayashi for providing native judgments, and Marcus Dickinson for comments on an early draft.; 16th European Conference on Machine Learning ; Conference date: 03-10-2005 Through 07-10-2005",

year = "2005",

month = jan,

day = "1",

doi = "10.1007/11564096_11",

language = "English",

isbn = "978-3-540-29243-2",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "60--71",

booktitle = "Machine Learning: ECML 2005",

address = "Germany",

url = "http://www.inescporto.pt/~jgama/ecmlpkdd05/",

}

RIS

TY - CHAP

T1 - Multi-view discriminative sequential learning

AU - Brefeld, Ulf

AU - Büscher, Christoph

AU - Scheffer, Tobias

N1 - Conference code: 16

PY - 2005/1/1

Y1 - 2005/1/1

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 semi-supervised hidden Markov perceptron, and a semi-supervised hidden Markov support vector learning algorithm. Experiments reveal that the resulting procedures utilize unlabeled data effectively and discriminate more accurately than their 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 semi-supervised hidden Markov perceptron, and a semi-supervised hidden Markov support vector learning algorithm. Experiments reveal that the resulting procedures utilize unlabeled data effectively and discriminate more accurately than their purely supervised counterparts.

KW - Informatics

KW - Unlabeled Data

KW - Neural Information Processing System

KW - Name Entity Recognition

KW - Entity Recognition

KW - Word Sense Disambiguation

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=33646415916&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/f5846d33-6389-3b3a-b23a-bdde947af373/

U2 - 10.1007/11564096_11

DO - 10.1007/11564096_11

M3 - Article in conference proceedings

AN - SCOPUS:33646415916

SN - 978-3-540-29243-2

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 60

EP - 71

BT - Machine Learning: ECML 2005

PB - Springer

T2 - 16th European Conference on Machine Learning

Y2 - 3 October 2005 through 7 October 2005

ER -

DOI

https://doi.org/10.1007/11564096_11
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Multi-view discriminative sequential learning

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