Multi-Task Learning via Structured Regularization: Formulations, Algorithms, and Applications
abstract: Multi-task learning (MTL) aims to improve the generalization performance (of the resulting classifiers) by learning multiple related tasks simultaneously. Specifically, MTL exploits the intrinsic task relatedness, based on which the informative domain knowledge from each task can be shared...
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| Format: | Doctoral Thesis |
| Language: | English |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/2286/R.I.9391 |