Abstract
We detail an exploratory experiment aimed at determining the performance of stochastic vector quantisation as a purely fusion methodology, in contrast to its performance as a composite classification/fusion mechanism. To achieve this we obtain an initial pattern space for which a simulated PDF is generated: a well-factored SVQ classifier then acts as a composite classifier/classifier fusion system in order to provide an overall representation rate. This performance is then contrasted with that of the individual classifiers (constituted by the factored code-vectors) acting in combination via conventional combination mechanisms. In this way, we isolate the performance of networked-SVQs as a purely combinatory mechanism for the base classifiers.
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© 2005 Springer-Verlag Berlin Heidelberg
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Patenall, R., Windridge, D., Kittler, J. (2005). Multiple Classifier Fusion Performance in Networked Stochastic Vector Quantisers. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2005. Lecture Notes in Computer Science, vol 3541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494683_13
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DOI: https://doi.org/10.1007/11494683_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26306-7
Online ISBN: 978-3-540-31578-0
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