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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Patenall, R., Windridge, D., & Kittler, J. (2005). Multiple classifier fusion performance in networked stochastic vector quantisers. In Lecture Notes in Computer Science (Vol. 3541, pp. 128–135). Springer Verlag. https://doi.org/10.1007/11494683_13
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