Abstract
This paper presents pattern reasoning, that is, the logical reasoning of patterns. Pattern reasoning is a new solution for the knowledge acquisition problem. Knowledge acquisition tried to acquire linguistic rules from patterns. In contrast, we try to modify logics to reason patterns. Patterns are represented as functions, which are approximated by neural networks. Therefore, pattern reasoning is realized by logical reasoning of neural networks. A few non-classical logics can reason neural networks, because neural networks can be basically regarded as multilinear functions and the logics are complete for multilinear function space, therefore, the logics can reason neural networks. This paper explains intermediate logic LC as an example of the logics and demonstrates how neural networks can be reasoned by LC.
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Tsukimoto, H. (1999). Pattern Reasoning: A New Solution for Knowledge Acquisition Problem. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_39
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DOI: https://doi.org/10.1007/978-3-540-48061-7_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66645-5
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