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Abstract: We learn multiple hypotheses for related tasks under a latent hierarchicalrelationship between tasks. We exploit the intuition that for domainadaptation, we wish to share classifier structure, but for multitask learning,we wish to share covariance structure. Our hierarchical model is seen tosubsume several previously proposed multitask learning models and performs wellon three distinct real-world data sets.



Autor: Hal Daumé III

Fuente: https://arxiv.org/



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