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  Competition and Multiple Cause Models

Dayan, P., & Zemel, R. (1995). Competition and Multiple Cause Models. Neural computation, 7(3), 565-579. doi:10.1162/neco.1995.7.3.565.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-D6C5-E Version Permalink: http://hdl.handle.net/21.11116/0000-0002-D6C6-D
Genre: Journal Article

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Dayan, P1, Author              
Zemel, RS, Author
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1External Organizations, ou_persistent22              

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 Abstract: If different causes can interact on any occasion to generate a set of patterns, then systems modeling the generation have to model the interaction too. We discuss a way of combining multiple causes that is based on the Integrated Segmentation and Recognition architecture of Keeler et al. (1991). It is more cooperative than the scheme embodied in the mixture of experts architecture, which insists that just one cause generate each output, and more competitive than the noisy-or combination function, which was recently suggested by Saund (1994a,b). Simulations confirm its efficacy.

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 Dates: 1995-05
 Publication Status: Published in print
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 Identifiers: DOI: 10.1162/neco.1995.7.3.565
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Title: Neural computation
Source Genre: Journal
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Publ. Info: Cambridge, Mass. : MIT Press
Pages: - Volume / Issue: 7 (3) Sequence Number: - Start / End Page: 565 - 579 Identifier: ISSN: 0899-7667
CoNE: https://pure.mpg.de/cone/journals/resource/954925561591