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Learning Multiple Tasks with Kernel Methods (2005)
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| Venue: | Journal of Machine Learning Research |
| Citations: | 250 - 10 self |
BibTeX
@ARTICLE{Evgeniou05learningmultiple,
author = {Theodoros Evgeniou and Charles A. Micchelli and Massimiliano Pontil},
title = {Learning Multiple Tasks with Kernel Methods},
journal = {Journal of Machine Learning Research},
year = {2005},
volume = {6},
pages = {615--637}
}
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Abstract
Editor: John Shawe-Taylor 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.
Keyphrases
kernel method multi-task learning multiple task many related task multi-task kernel function multiple related task support vector machine john shawe-taylor novel form past empirical work standard single-task learning single task learning problem regularization network specific kernel many task function real data set standard single-task kernel method
