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Learning multiple tasks with kernel methods

Evgeniou, T; Micchelli, CA; Pontil, M; (2005) Learning multiple tasks with kernel methods. J MACH LEARN RES , 6 615 - 637. Gold open access


We study the problem of learning many related tasks simultaneously using kernel methods and regularization. The standard single-task kernel methods, such as support vector machines and regularization networks, are extended to the case of multi-task learning. Our analysis shows that the problem of estimating many task functions with regularization can be cast as a single task learning problem if a family of multi-task kernel functions we define is used. These kernels model relations among the tasks and are derived from a novel form of regularizers. Specific kernels that can be used for multi-task learning are provided and experimentally tested on two real data sets. In agreement with past empirical work on multi-task learning, the experiments show that learning multiple related tasks simultaneously using the proposed approach can significantly outperform standard single-task learning particularly when there are many related tasks but few data per task.

Type: Article
Title: Learning multiple tasks with kernel methods
Open access status: An open access publication
Publisher version: http://www.jmlr.org/
Keywords: multi-task learning, kernels, vector-valued functions, regularization, learning algorithms, MODEL, BIAS
URI: http://discovery.ucl.ac.uk/id/eprint/13423
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