Using test plans for bayesian modeling
Deventer R, Denzler J, Niemann H, Kreis O (2003)
Publication Language: English
Publication Status: Published
Publication Type: Conference contribution, Conference Contribution
Publication year: 2003
Publisher: Springer
City/Town: Berlin
Book Volume: 2734
Pages Range: 307-316
Conference Proceedings Title: Machine Learning and Data Mining in Pattern Recognition
Event location: Leipzig
URI: https://www.scopus.com/record/display.uri?eid=2-s2.0-8344245480∨igin=inward
Abstract
When modeling technical processes, the training data regularly come from test plans, to reduce the number of experiments and to save time and costs. On the other hand, this leads to unobserved combinations of the input variables. In this article it is shown, that these unobserved configurations might lead to un-trainable parameters. Afterwards a possible design criterion is introduced, which avoids this drawback. Our approach is tested to model a welding process. The results show, that hybrid Bayesian networks are able to deal with yet unobserved in- and output data.
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APA:
Deventer, R., Denzler, J., Niemann, H., & Kreis, O. (2003). Using test plans for bayesian modeling. In Perner P.; Rosenfeld A. (Eds.), Machine Learning and Data Mining in Pattern Recognition (pp. 307-316). Leipzig, DE: Berlin: Springer.
MLA:
Deventer, Rainer, et al. "Using test plans for bayesian modeling." Proceedings of the Third International Conference, MLDM 2003, Leipzig Ed. Perner P.; Rosenfeld A., Berlin: Springer, 2003. 307-316.
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