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Published Articles >> Table of Contents >> Abstract
17th International Conference on Pattern Recognition (ICPR'04) - Volume 2
pp. 48-51
Joint Spatial and Temporal Structure Learning for Task based Control
Kingsley Sage, University of Sussex, UK
Hilary Buxton, University of Sussex, UK
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.1334032
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| Abstract |
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We present an example of a joint spatial and temporal task learning algorithm that results in a generative model that has applications for on-line visual control. We review work on learning transformed mixture of gaussians (due to Frey and Jojic) and Variable Length Markov Models (VLMMS due to Ron, Singer and Tishby). We show how a temporal model, learned through an extension of VLMMs to deal with multinomially distributed input symbol vectors, can be used as an improvement on Maximum Likelihood (ML) for prior parameter estimation for the Expectation Maximisation (EM) process.
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Citation:
Kingsley Sage, Hilary Buxton,
"Joint Spatial and Temporal Structure Learning for Task based Control,"
icpr,
pp. 48-51,
17th International Conference on Pattern Recognition (ICPR'04) - Volume 2,
2004
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