ICML-06 Workshop on

Structural Knowledge Transfer for Machine Learning

Thursday, June-29, Carnegie Mellon University

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Rationale

Applying knowledge learned in one task or domain (the source) to improve learning on another related task or domain (the target) has been the center-stage in education research. Our concept of schooling is based on the assumption that what children learn early on impacts their learning and actions in the later grades.

The benefits of knowledge transfer has engaged cognitive psychologists for over a century. Although this underlying philosophy has also been a long-time inspiration to machine learning and artificial intelligence in general, there has been a renewed interest in knowledge transfer only recently. It is now an active research area that requires new approaches, formalisms, algorithms, and testbeds. Traditional machine learning considers problems drawn from the same population, while transfer learning considers problems from different populations and thus raises machine learning to a higher and more challenging level. Transfer can be especially effective when the learned knowledge is suitably structured, for example, in a relational or hierarchical fashion. This workshop is devoted to transfer learning in all subareas of machine learning, including but not limited to, concept learning, clustering, reinforcement learning, analogy, and to applications and evaluation methodologies of transfer learning, with an emphasis on how the learned knowledge is structured and exploited.

What's  New

bullet Workshop Schedule updated (Jun-28).
bullet Online Proceedings available (Jun-26).
bullet Workshop Schedule available (Jun-13).
bullet Abstract and bio of Invited Speakers available (Jun-13).
bullet Acceptance/rejection decisions have been emailed (May-26).
bullet Paper submission is closed. Acknowledgements have been sent to contact authors (May-08).
bullet Attendance policy declared (May-05). See Call for contributions.

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Last updated: 06/13/06.