To improve recognition results, decisions of multiple neural networks can be aggregated into a committee decision. In contrast to the ordinary approach of utilizing all neural networks available to make a committee decision, we propose creating adaptive committees, which are specific for each input data point. A prediction network is used to identify classification neural networks to be fused for making a committee decision about a given input data point. The jth output value of the prediction network expresses the expectation level that the jth classification neural network will make a correct decision about the class label of a given input data point. The effectiveness of the approach is demonstrated on two artificial and three real data sets. © Springer-Verlag Berlin Heidelberg 2002.
CITATION STYLE
Verikas, A., Lipnickas, A., & Malmqvist, K. (2002). Selecting neural networks for making a committee decision. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 420–425). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_68
Mendeley helps you to discover research relevant for your work.