Published 1998
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Incorporating Test Inputs into Learning
Abstract
In many applications, such as credit default prediction and medical image recognition, test inputs are available in addition to the labeled training examples. We propose a method to incorporate the test inputs into learning. Our method results in solutions having smaller test errors than that of simple training solution, especially for noisy problems or small training sets.
Additional Information
© 1998 Massachusetts Institute of Technology. We would like to thank the Caltech Learning Systems Group: Prof. Yaser Abu-Mostafa, Dr. Amir Atiya, Alexander Nicholson, Joseph Sill and Xubo Song for many useful discussions.Attached Files
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Additional details
- Eprint ID
- 64700
- Resolver ID
- CaltechAUTHORS:20160223-162630923
- Created
-
2016-02-24Created from EPrint's datestamp field
- Updated
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2019-10-03Created from EPrint's last_modified field
- Series Name
- Advances in Neural Information Processing Systems
- Series Volume or Issue Number
- 10