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  Inferring latent task structure for Multitask Learning by Multiple Kernel Learning

Widmer, C., Toussaint, N., Altun, Y., & Rätsch, G. (2010). Inferring latent task structure for Multitask Learning by Multiple Kernel Learning. BMC Bioinformatics, 11(Supplement 8): S5. doi:10.1186/1471-2105-11-S8-S5.

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 Creators:
Widmer, C1, Author              
Toussaint, NC, Author
Altun, Y2, 3, Author              
Rätsch, G1, Author              
Affiliations:
1Rätsch Group, Friedrich Miescher Laboratory, Max Planck Society, ou_3378052              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
3Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: Background The lack of sufficient training data is the limiting factor for many Machine Learning applications in Computational Biology. If data is available for several different but related problem domains, Multitask Learning algorithms can be used to learn a model based on all available information. In Bioinformatics, many problems can be cast into the Multitask Learning scenario by incorporating data from several organisms. However, combining information from several tasks requires careful consideration of the degree of similarity between tasks. Our proposed method simultaneously learns or refines the similarity between tasks along with the Multitask Learning classifier. This is done by formulating the Multitask Learning problem as Multiple Kernel Learning, using the recently published q-Norm MKL algorithm. Results We demonstrate the performance of our method on two problems from Computational Biology. First, we show that our method is able to improve performance on a splice site dataset with given hierarchical task structure by refining the task relationships. Second, we consider an MHC-I dataset, for which we assume no knowledge about the degree of task relatedness. Here, we are able to learn the task similarities ab initio along with the Multitask classifiers. In both cases, we outperform baseline methods that we compare against. Conclusions We present a novel approach to Multitask Learning that is capable of learning task similarity along with the classifiers. The framework is very general as it allows to incorporate prior knowledge about tasks relationships if available, but is also able to identify task similarities in absence of such prior information. Both variants show promising results in applications from Computational Biology.

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 Dates: 2010-10
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1186/1471-2105-11-S8-S5
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Title: NIPS Workshop on Machine Learning in Computational Biology (MLCB 2019)
Place of Event: Whistler, BC, Canada
Start-/End Date: 2009-12-10 - 2009-12-11

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Title: BMC Bioinformatics
Source Genre: Journal
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Affiliations:
Publ. Info: BioMed Central
Pages: 8 Volume / Issue: 11 (Supplement 8) Sequence Number: S5 Start / End Page: - Identifier: ISSN: 1471-2105
CoNE: https://pure.mpg.de/cone/journals/resource/111000136905000