Sep
14
2009

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  • Speaker: Hao Zhou
  • Host Department: Statistics
  • Date: 09/14/2009
  • Time: 3:00 PM

  • Location: 438 West Hall

  • Description:

    Title: Generalized Linear Model Approach to Multi-Task Learning
    Advisor: Professor George Michailidis
    Committee Members: Associate Professor Kerby Shedden, Associate Professor Ji Zhu

    Abstract: Multi-task learning is a machining learning method that improves learning of signal task by using information from other train sets. In this proposal, we explain how multi-task learning works and give an overview of existing multi-task learning methods and some theoretical backgrounds. We propose a new multi-task learning method based on generalized linear models and discuss the possible steps of statistical inference.

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