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Monotonicity Hints

Sill, Joseph and Abu-Mostafa, Yaser S. (1997) Monotonicity Hints. In: Advances in Neural Information Processing Systems 9 (NIPS 1996). Advances in Neural Information Processing Systems. No.9. MIT Press , Cambridge, MA, pp. 634-640. ISBN 0-262-10065-7. https://resolver.caltech.edu/CaltechAUTHORS:20160223-161511946

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Abstract

A hint is any piece of side information about the target function to be learned. We consider the monotonicity hint, which states that the function to be learned is monotonic in some or all of the input variables. The application of monotonicity hints is demonstrated on two real-world problems- a credit card application task, and a problem in medical diagnosis. A measure of the monotonicity error of a candidate function is defined and an objective function for the enforcement of monotonicity is derived from Bayesian principles. We report experimental results which show that using monotonicity hints leads to a statistically significant improvement in performance on both problems.


Item Type:Book Section
Related URLs:
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http://papers.nips.cc/paper/1270-monotonicity-hintsOrganizationPaper
Additional Information:© 1997 Massachusetts Institute of Technology.
Series Name:Advances in Neural Information Processing Systems
Issue or Number:9
Record Number:CaltechAUTHORS:20160223-161511946
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20160223-161511946
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:64696
Collection:CaltechAUTHORS
Deposited By: Kristin Buxton
Deposited On:24 Feb 2016 00:18
Last Modified:03 Oct 2019 09:40

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