References of "Neurocomputing"
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See detailReservoir Computing for Detection of Steady State in Performance Tests of Compressors
Antonelo, Eric Aislan UL; Flesch, Carlos; Filipe, Schmitz

in Neurocomputing (2017)

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See detailSelf-organizing mixture models
Verbeek, J. J.; Vlassis, Nikos UL; Krose, B. J. A.

in Neurocomputing (2005), 63

We present an expectation-maximization (EM) algorithm that yields topology preserving maps of data based on probabilistic mixture models. Our approach is applicable to any mixture model for which we have ... [more ▼]

We present an expectation-maximization (EM) algorithm that yields topology preserving maps of data based on probabilistic mixture models. Our approach is applicable to any mixture model for which we have a normal EM algorithm. Compared to other mixture model approaches to self-organizing maps (SOMs), the function our algorithm maximizes has a clear interpretation: it sums data log-likelihood and a penalty term that enforces self-organization. Our approach allows principled handling of missing data and learning of mixtures of SOMs. We present example applications illustrating our approach for continuous, discrete, and mixed discrete and continuous data. (C) 2004 Elsevier B.V. All rights reserved. [less ▲]

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