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Inferring latent task structure for Multitask Learning by Multiple Kernel Learning

2010

Article

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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.

Author(s): Widmer, C. and Toussaint, NC. and Altun, Y. and Rätsch, G.
Journal: BMC Bioinformatics
Volume: 11 Suppl 8
Pages: S5
Year: 2010
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1186/1471-2105-11-S8-S5

Links: Web

BibTex

@article{WidmerTAR2010,
  title = {Inferring latent task structure for Multitask Learning by Multiple Kernel Learning},
  author = {Widmer, C. and Toussaint, NC. and Altun, Y. and R{\"a}tsch, G.},
  journal = {BMC Bioinformatics},
  volume = {11 Suppl 8},
  pages = {S5},
  year = {2010}
}