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Nature 428, 378 (25 March 2004) | doi:10.1038/428378a
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Postdoctoral Research Fellows
- Northwestern University
- Chicago, Illinois, United States
Post Doctoral Research Associate
- University of Illinois
- Urbana United States
Learning theory: Past performance and future results
Carlo Tomasi1
Abstract
Learning from experience is hard, and predicting how well what we have learned will serve us in the future is even harder. The most useful lessons turn out to be those that are insensitive to small changes in our experience.
A hallmark of intelligent learning is that we can apply what we have learned to new situations. In the mathematical theory of learning, this ability is called generalization.
- Department of Computer Science, Duke University, Durham, North Carolina 27708, USA.
Email: tomasi@cs.duke.edu
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RESEARCH
General conditions for predictivity in learning theoryNature Letters to Editor (25 Mar 2004)
