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  Partially-supervised context-specific independence mixture modeling.

Georgi, B., & Schliep, A. (n.d.). Partially-supervised context-specific independence mixture modeling. In P.-O.-T.-S.-I.-W.-O.-M.-R.-R.-D. MINING (Ed.), ECML. Berlin/Heidelberg: Springer.

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Georgi2007c.pdf (Any fulltext), 215KB
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 Creators:
Georgi, Benjamin1, Author
Schliep, Alexander2, Author              
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1Max Planck Society, ou_persistent13              
2Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433547              

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 Abstract: Partially supervised or semi-supervised learning refers to machine learning methods which fall between clustering and classification. In the context of clustering, labels can specify link and do-not-link constraints between data points in di erent ways and constrain the resulting clustering solutions. This is a very natural framework for many biological applications as some labels are often available and even very few label greatly improve clustering results. Context-specific independence models constitute a framework for simultaneous mixture estimation and model structure determination to obtain meaningful models for high-dimensional data with many, possibly uninformative, variables. Here we present the first approach for partial learning of CSI models and demonstrate the e ectiveness of modest amounts of labels for simulated data and for protein sub-family determination.

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Language(s): eng - English
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 Publication Status: Not specified
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Title: Data mining in functional genomics and proteomics : Current trends and future directions
Place of Event: Warsaw, Poland
Start-/End Date: 2007-09-17 - 2007-09-17

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Title: ECML
Source Genre: Proceedings
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MINING, PROCEEDINGS OF THE SIXTH INTERNATIONAL WORKSHOP ON MULTI-RELATIONAL RELATIONAL DATA, Editor
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Publ. Info: Berlin/Heidelberg : Springer
Pages: 10 Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: -

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Title: Lecture Notes in Computer Science
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