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A Method for Learning from Hints

Abu-Mostafa, Yaser S. (1993) A Method for Learning from Hints. In: Advances in Neural Information Processing Systems 5 (NIPS 1992). Advances in Neural Information Processing Systems. No.5. Morgan Kaufmann , San Mateo, CA, pp. 73-80. ISBN 1-55860-274-7.

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We address the problem of learning an unknown function by putting together several pieces of information (hints) that we know about the function. We introduce a method that generalizes learning from examples to learning from hints. A canonical representation of hints is defined and illustrated for new types of hints. All the hints are represented to the learning process by examples, and examples of the function are treated on equal footing with the rest of the hints. During learning, examples from different hints are selected for processing according to a given schedule. We present two types of schedules; fixed schedules that specify the relative emphasis of each hint, and adaptive schedules that are based on how well each hint has been learned so far. Our learning method is compatible with any descent technique that we may choose to use.

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Additional Information:© 1993 Morgan Kaufmann. The author would like to thank Ms. Zehra Kok for her valuable input. This work was supported by the AFOSR under grant number F49620-92-J-0398.
Funding AgencyGrant Number
Air Force Office of Scientific Research (AFOSR)F49620-92-J-0398
Series Name:Advances in Neural Information Processing Systems
Issue or Number:5
Record Number:CaltechAUTHORS:20160128-163222557
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:64070
Deposited By: Kristin Buxton
Deposited On:29 Jan 2016 17:18
Last Modified:03 Oct 2019 09:34

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