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IJCNN
2000
IEEE

Bias Learning, Knowledge Sharing

13 years 8 months ago
Bias Learning, Knowledge Sharing
—Biasing properly the hypothesis space of a learner has been shown to improve generalization performance. Methods for achieving this goal have been proposed, that range from designing and introducing a bias into a learner to automatically learning the bias. Multitask learning methods fall into the latter category. When several related tasks derived from the same domain are available, these methods use the domain-related knowledge coded in the training examples of all the tasks as a source of bias. We extend some of the ideas presented in this field and describe a new approach that identifies a family of hypotheses, represented by a manifold in hypothesis space, that embodies domain-related knowledge. This family is learned using training examples sampled from a group of related tasks. Learning models trained on these tasks are only allowed to select hypotheses that belong to the family. We show that the new approach encompasses a large variety of families which can be learned. A stat...
Joumana Ghosn, Yoshua Bengio
Added 31 Jul 2010
Updated 31 Jul 2010
Type Conference
Year 2000
Where IJCNN
Authors Joumana Ghosn, Yoshua Bengio
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