Abstract
Incremental learning gets increasingly important to cope with systems of high complexity or to adapt to changing environmental conditions. But to assure safety, the process of incremental learning must be supervised so that no knowledge learned incorrectly or under uncertain conditions influences the system in a contra-productive way. Hence we consider two principles to estimate different kinds of uncertainty, or in other words the trustworthiness, of an incrementally learning system. They are investigated principally for a simplified scenario that explicitly covers all different kinds of uncertainties. Finally, a combined measure to reflect all uncertainties of an incrementally learning system is presented.
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Buschermöhle, A., Schoenke, J., Brockmann, W. (2012). Uncertainty and Trust Estimation in Incrementally Learning Function Approximation. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances on Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31709-5_4
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DOI: https://doi.org/10.1007/978-3-642-31709-5_4
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