Abstract
We consider fixed non–linear combining rules as a strategy for information fusion in neural network based machine learning problems and compare them with adaptive ones. In this strategy, essential part of the work is overloaded to examiner–operator who ought to split the data “reliably”. In small sample size situations, non–trainable combination rules allow creating committee with performance comparable or even lower with that obtained with more sophisticated information fusion methods. Fixed rule’s solutions are easier interpreted by end users.
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Raudys, S., Pabarskaite, Z. (2004). Fixed Non–linear Combining Rules versus Adaptive Ones. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_35
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DOI: https://doi.org/10.1007/978-3-540-24844-6_35
Publisher Name: Springer, Berlin, Heidelberg
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