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A General Logical Approach to Learning from Time Series (Invited Talk)

Author Guido Sciavicco

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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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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.

Cite As Get BibTex

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

Author Details

Guido Sciavicco
  • Department of Mathematics and Computer Science, University of Ferrara, Italy

Funding

We acknowledge the support of the FIRD project Methodological Developments in Modal Symbolic Geometric Learning, funded by the University of Ferrara, and the INDAM-GNCS project Symbolic and Numerical Analysis of Cyberphysical Systems (code CUP_E53C23001670001), funded by INDAM; Guido Sciavicco is a GNCS-INdAM member. Moreover, this research has also been funded by the Italian Ministry of University and Research through PNRR - M4C2 - Investimento 1.3 (Decreto Direttoriale MUR n. 341 del 15/03/2022), Partenariato Esteso PE00000013 - "FAIR - Future Artificial Intelligence Research" - Spoke 8 "Pervasive AI", funded by the European Union under the "NextGeneration EU programme".

References

  1. D. Del Fante, F. Manzella, G. Sciavicco, and I. E. Stan. A post-modern approach to automatic metaphor identification. In Proc. of the 9th Italian Conference on Computational Linguistics (CLIC), volume 3596 of CEUR Workshop Proceedings. CEUR-WS.org, 2023. URL: https://ceur-ws.org/Vol-3596/short9.pdf.
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  3. F. Manzella, G. Pagliarini, G. Sciavicco, and I. E. Stan. The voice of COVID-19: breath and cough recording classification with temporal decision trees and random forests. Artif. Intell. Medicine, 137:102486, 2023. URL: https://doi.org/10.1016/J.ARTMED.2022.102486.
  4. S. Mazzacane, M. Coccagna, F. Manzella, G. Pagliarini, V. A. Sironi, A. Gatti, E. Caselli, and G. Sciavicco. Towards an objective theory of subjective liking: A first step in understanding the sense of beauty. PLOS ONE, 18(6):1-20, June 2023. Google Scholar
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