Regularized multi--task learning

  title={Regularized multi--task learning},
  author={Theodoros Evgeniou and Massimiliano Pontil},
Past empirical work has shown that learning multiple related tasks from data simultaneously can be advantageous in terms of predictive performance relative to learning these tasks independently. In this paper we present an approach to multi--task learning based on the minimization of regularization functionals similar to existing ones, such as the one for Support Vector Machines (SVMs), that have been successfully used in the past for single--task learning. Our approach allows to model the… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.

Explore Further: Topics Discussed in This Paper


Publications referenced by this paper.

Improving parameter estimates and model prediction by aggregate customization in choice experiments

  • N. Arora, J. Huber
  • Journal of Consumer Research,
  • 2001
Highly Influential
4 Excerpts

A Model for Inductive Bias Learning

  • J. Baxter
  • Journal of Artificial Intelligence Research,
  • 2000
Highly Influential
5 Excerpts

A Hierarchical Bayes Model of Primary and Secondary Demand

  • N. Arora G.M Allenby, J. Ginter
  • Marketing Science,
  • 1998
Highly Influential
10 Excerpts

Similar Papers

Loading similar papers…