@inproceedings{
    Kaski07ecml,
    editor = "Kok, Joost N. and Koronacki, Jacek and {Lopez de Mantaras}, Ramon and Matwin, Stan and Mladeni{\v c}, Dunja and Skowron, Andrzej",
    author = "Kaski, Samuel and Peltonen, Jaakko",
    publisher = "Springer-Verlag",
    title = "Learning from Relevant Tasks Only",
    url = "http://dx.doi.org/10.1007/978-3-540-74958-5_59",
    booktitle = "Machine Learning: ECML 2007 (Proceedings of the 18th European Conference on Machine Learning)",
    address = "Berlin Heidelberg",
    abstract = "We introduce a problem called relevant subtask learning, a variant of multi-task learning. The goal is to build a classifier for a task-of-interest having too little data. We also have data for other tasks but only some are relevant, meaning they contain samples classified in the same way as in the task-of-interest. The problem is how to utilize this ``background data'' to improve the classifier in the task-of-interest. We show how to solve the problem for logistic regression classifiers, and show that the solution works better than a comparable multi-task learning model. The key is to assume that data of all tasks are mixtures of relevant and irrelevant samples, and model the irrelevant part with a sufficiently flexible model such that it does not distort the model of relevant data.\par $\copyright$2007 Springer-Verlag, \url{http://www.springer.com/series/1244/} \par This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. \par The authors belong to Helsinki Institute for Information Technology and the Adaptive Informatics Research Centre. They were supported by the Academy of Finland, decision numbers 108515 and 207467. This work was also supported in part by the IST Programme of the European Community, PASCAL Network of Excellence, IST-2002-506778. This publication only reflects the authors' views. All rights are reserved because of other commitments.",
    note = "Preprint pdf at \url{http://www.cis.hut.fi/projects/mi/papers/ecml07.pdf}",
    flags = "AIRC copy",
    year = "2007",
    impactfactor = "D4",
    pages = "608--615"
}