LIPIcs.TIME.2024.1.pdf
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A General Logical Approach to Learning from Time Series (Invited Talk)
Author
Guido Sciavicco 
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31st International Symposium on Temporal Representation and Reasoning (TIME 2024)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Symposium on Temporal Representation and Reasoning (TIME) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2024-10-22
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Guido Sciavicco. A General Logical Approach to Learning from Time Series (Invited Talk). In 31st International Symposium on Temporal Representation and Reasoning (TIME 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 318, pp. 1:1-1:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.TIME.2024.1
https://doi.org/10.4230/LIPIcs.TIME.2024.1
Abstract
Machine learning from multivariate time series is a common task, and countless different approaches to typical learning problems have been proposed in recent years. In this talk, we review some basic ideas towards logic-based learning methods, and we sketch a general framework.
Subject Classification
ACM Subject Classification
- Theory of computation → Theory and algorithms for application domains
Keywords
- Machine learning
- temporal logic
- general approach
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References
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