@inproceedings{
    Kaski07ecml,
    editor = "Kok, Joost N. and Koronacki, Jacek and {Lopez de Mantaras}, Ramon and Matwin, Stan and Mladenic, 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",
    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.",
    flags = "AIRC HIIT public",
    year = "2007",
    impactfactor = "D4",
    pages = "608-615"
}