Graphical Multi-Task Learning
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We investigate the problem of learning multiple tasks that are related according to a network structure, using the multi-task kernel framework proposed by Evgeniou, Micchelli and Pontil. Our method combines a graphical task kernel with an arbitrary base kernel.We demonstrate its effectiveness on a real ecological application that inspired this work.
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NSF Award No. 0514429, AFOSR Award No. FA9550-07-1-0124
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2008-10-31T20:34:49Z
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Multi-Task Learning; Networks
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