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Graphical Multi-Task Learning
Abstract
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.
Date Issued
2008-10-31Subject
Multi-Task Learning; Networks
Type
report