Abstract
In this paper, we investigate prediction of recursive real-valued functions from finite examples by extending the framework of inductive inference of recursive real-valued functions to be a more realistic one. First, we propose a finite prediction machine, which is a procedure that requests finite examples of a recursive real-valued function h and a datum of a real number x, and that outputs a datum of h(x). Then, we formulate finite prediction of recursive real-valued functions and investigate the power of it. Furthermore, for a fixed rational closed interval I, we show that the class of all finitely predictable sets of recursive real-valued functions coincides with the class of all inferable sets of recursive real-valued functions in the limit, that is, \({\sc RealFP}_{\emph I}={\sc RealEx}_{\emph I}\).
This work is partially supported by Grand-in-Aid for Scientific Research 15700137 and 16500084 from the Ministry of Education, Culture, Sports, Science and Technology, Japan.
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Hirowatari, E., Hirata, K., Miyahara, T. (2006). Prediction of Recursive Real-Valued Functions from Finite Examples. In: Washio, T., Sakurai, A., Nakajima, K., Takeda, H., Tojo, S., Yokoo, M. (eds) New Frontiers in Artificial Intelligence. JSAI 2005. Lecture Notes in Computer Science(), vol 4012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11780496_25
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DOI: https://doi.org/10.1007/11780496_25
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