Material Detail

Multitask learning: the Bayesian way

Multitask learning: the Bayesian way

This video was recorded at Open House on Multi-Task and Complex Outputs Learning, London 2006. Multi-task learning lends itself particularly well to a Bayesian approach. Cross-inference between tasks can be implemented by sharing parameters in the likelihood model and the prior for the task-specific model parameters. Choosing different priors, one can implement task clustering and task gating. Throughout my presentation, predicting single-copy newspaper sales will serve as a running example.

Quality

  • User Rating
  • Comments
  • Learning Exercises
  • Bookmark Collections
  • Course ePortfolios
  • Accessibility Info

More about this material

Comments

Log in to participate in the discussions or sign up if you are not already a MERLOT member.