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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

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 across multiple tasks and thus facilitate the individual task learning. It is particularly desirable to share the domain knowledge (among the tasks) when there are a number of related tasks but only limited training data is available for each task.

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    Date Created
    • 2011
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  • Text
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    Note
    • Partial requirement for: Ph.D., Arizona State University, 2011
      Note type
      thesis
    • Includes bibliographical references (P. 122-131)
      Note type
      bibliography
    • Field of study: Computer science

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    Jianhui Chen

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