Abstract
In this paper we present a Method for Inductive Cost Optimization (MICO), as an example of induction biased by using background knowledge. The method produces a decision tree that identifies those setpoints that enable the process to produce in as cost-efficient a manner as possible. We report on two examples, one idealised and one real-world. Some problems concerning MICO are reported.
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© 1991 Springer-Verlag Berlin Heidelberg
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Verdenius, F. (1991). A method for inductive cost optimization. In: Kodratoff, Y. (eds) Machine Learning — EWSL-91. EWSL 1991. Lecture Notes in Computer Science, vol 482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0017013
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DOI: https://doi.org/10.1007/BFb0017013
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