Miller, John W. and Goodman, Rod and Smyth, Padhraic (1991) Objective functions for probability estimation. In: IJCNN-91-Seattle International Joint Conference on Neural Networks. Vol.1. IEEE , Piscataway, NJ, pp. 881-886. ISBN 0780301641. https://resolver.caltech.edu/CaltechAUTHORS:20190314-142000675
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Abstract
Backpropagation was originally derived in the context of minimizing a mean-squared error (MSE) objective function. More recently there has been interest in objective functions that provide accurate class probability estimates. In this paper we derive necessary and sufficient conditions on the required form of an objective function to provide probability estimates. This leads to the definition of a general class of functions which includes MSE and cross cutropy (CE) as two of the simplest cases.
| Item Type: | Book Section | ||||||||||
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| Additional Information: | © 1991 IEEE. The research described in this paper was supported in part by DARPA under grant number AFOSR-90-0199 and by NSF grant ENG-8711673. In addition this work was carried out in part by the Jet Propulsion Laboratories, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. | ||||||||||
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| Record Number: | CaltechAUTHORS:20190314-142000675 | ||||||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20190314-142000675 | ||||||||||
| Official Citation: | J. W. Miller, R. Goodman and P. Smyth, "Objective functions for probability estimation," IJCNN-91-Seattle International Joint Conference on Neural Networks, Seattle, WA, USA, 1991, pp. 881-886 vol.1. doi: 10.1109/IJCNN.1991.155295 | ||||||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||||||
| ID Code: | 93835 | ||||||||||
| Collection: | CaltechAUTHORS | ||||||||||
| Deposited By: | George Porter | ||||||||||
| Deposited On: | 14 Mar 2019 22:25 | ||||||||||
| Last Modified: | 03 Oct 2019 20:58 |
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