Abstract
In this paper we provide a framework for the design of a practical monitoring method with learning methods. We demonstrate that three medical and industrial monitoring problems involve subproblems that can be tackled with our approach. Application of interference removal, novelty detection and learning of a signature leads to a feasible monitoring method in these cases.
This work is supported by Technology Foundation STW, project DTN-44.3584. We thank TechnoFysica b.v. and Drs. Groeneveld, Stam and Frietman for cooperation.
A. Ypma and O. Baunbæk-Jensen are with the Dutch Foundation for Neural Networks SNN Nijmegen and the TU Denmark, respectively.
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Ypma, A., Melissant, C., Baunbæk-Jensen, O., Duin, R.P.W. (2001). Health Monitoring with Learning Methods. In: Dorffner, G., Bischof, H., Hornik, K. (eds) Artificial Neural Networks — ICANN 2001. ICANN 2001. Lecture Notes in Computer Science, vol 2130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44668-0_77
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DOI: https://doi.org/10.1007/3-540-44668-0_77
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