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Pearlmutter, Barak A. and Hinton, Geoffrey (1986) G-Maximization: an Unsupervised Learning Procedure for Discovering Regularities. AIP Conference Proceedings, 151. pp. 333-338. ISSN 0094-243X
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Abstract
Hill climbing is used to maximize an information theoretic measure of the difference
between the actual behavior of a unit and the behavior that would be predicted by a
statistician who knew the first order statistics of the inputs but believed them to be
independent. This causes the unit to detect higher order correlations among its inputs.
Initial simulations are presented, and seem encouraging. We describe an extension of the
basic idea which makes it resemble competitive learning and which causes members of a
population of these units to differentiate, each extracting different structure from the input.
| Item Type: | Article |
|---|---|
| Keywords: | G-Maximization; Unsupervised Learning Procedure; Discovering Regularities; |
| Academic Unit: | Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 5533 |
| Depositing User: | Barak Pearlmutter |
| Date Deposited: | 03 Nov 2014 16:07 |
| Journal or Publication Title: | AIP Conference Proceedings |
| Publisher: | American Institute of Physics |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/5533 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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