Sill, Joseph (1998) Monotonic Networks. In: Advances in Neural Information Processing Systems 10 (NIPS 1997). Advances in Neural Information Processing Systems. No.10. MIT Press , Cambridge, MA, pp. 661-667. ISBN 0-262-10076-2. https://resolver.caltech.edu/CaltechAUTHORS:20160223-163030288
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Abstract
Monotonicity is a constraint which arises in many application domains. We present a machine learning model, the monotonic network, for which monotonicity can be enforced exactly, i.e., by virtue of functional form . A straightforward method for implementing and training a monotonic network is described. Monotonic networks are proven to be universal approximators of continuous, differentiable monotonic functions. We apply monotonic networks to a real-world task in corporate bond rating prediction and compare them to other approaches.
| Item Type: | Book Section | ||||||
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| Additional Information: | © 1998 Massachusetts Institute of Technology. The author is very grateful to Yaser Abu-Mostafa for considerable guidance. I also thank John Moody for supplying the data. Amir Atiya, Eric Bax, Zehra Cataltepe, Malik Magdon-Ismail, Alexander Nicholson, and Xubo Song supplied many useful comments. | ||||||
| Series Name: | Advances in Neural Information Processing Systems | ||||||
| Issue or Number: | 10 | ||||||
| Record Number: | CaltechAUTHORS:20160223-163030288 | ||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20160223-163030288 | ||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||
| ID Code: | 64702 | ||||||
| Collection: | CaltechAUTHORS | ||||||
| Deposited By: | INVALID USER | ||||||
| Deposited On: | 24 Feb 2016 18:32 | ||||||
| Last Modified: | 03 Oct 2019 09:40 |
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