Multi-task learning for computational biology: Overview and outlook Book Section
| Authors: | Widmer, C.; Kloft, M.; Rätsch, G. |
| Editors: | Schölkopf, B.; Luo, Z.; Vovk, V. |
| Article/Chapter Title: | Multi-task learning for computational biology: Overview and outlook |
| Abstract: | We present an overview of the field of regularization-based multi-task learning, which is a relatively recent offshoot of statistical machine learning. We discuss the foundations as well as some of the recent advances of the field, including strategies for learning or refining the measure of task relatedness. We present an example from the application domain of Computational Biology, where multi-task learning has been successfully applied, and give some practical guidelines for assessing a priori, for a given dataset, whether or not multi-task learning is likely to pay off. © Springer-Verlag Berlin Heidelberg 2013. |
| Book Title: | Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik |
| ISBN: | 978-3-642-41135-9 |
| Publisher: | Springer |
| Publication Place: | Heidelberg, Germany |
| Date Published: | 2013-01-01 |
| Start Page: | 117 |
| End Page: | 127 |
| Language: | English |
| DOI: | 10.1007/978-3-642-41136-6_12 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | Book Chapter: 12 -- Export Date: 7 January 2016 -- Source: Scopus |
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