Gaussian process prediction for time series of structured data

Paaßen B, Göpfert C, Hammer B (2016)
In: Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed);Bruges: 41-46.

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Conference Paper | Published | English
Editor
Verleysen, Michele
Abstract
Time series prediction constitutes a classic topic in machine learning with wide-ranging applications, but mostly restricted to the domain of vectorial sequence entries. In recent years, time series of structured data (such as sequences, trees or graph structures) have become more and more important, for example in social network analysis or intelligent tutoring systems. In this contribution, we propose an extension of time series models to strucured data based on Gaussian processes and structure kernels. We also provide speedup techniques for predictions in linear time, and we evaluate our approach on real data from the domain of intelligent tutoring systems.
Publishing Year
Conference
24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Location
Bruges
Conference Date
2016-04-27 – 2016-04-29
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Paaßen B, Göpfert C, Hammer B. Gaussian process prediction for time series of structured data. In: Verleysen M, ed. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges; 2016: 41-46.
Paaßen, B., Göpfert, C., & Hammer, B. (2016). Gaussian process prediction for time series of structured data. In M. Verleysen (Ed.), Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 41-46). Bruges.
Paaßen, B., Göpfert, C., and Hammer, B. (2016). “Gaussian process prediction for time series of structured data” in Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ed. M. Verleysen (Bruges), 41-46.
Paaßen, B., Göpfert, C., & Hammer, B., 2016. Gaussian process prediction for time series of structured data. In M. Verleysen, ed. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, pp. 41-46.
B. Paaßen, C. Göpfert, and B. Hammer, “Gaussian process prediction for time series of structured data”, Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Bruges: 2016, pp.41-46.
Paaßen, B., Göpfert, C., Hammer, B.: Gaussian process prediction for time series of structured data. In: Verleysen, M. (ed.) Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. p. 41-46. Bruges (2016).
Paaßen, Benjamin, Göpfert, Christina, and Hammer, Barbara. “Gaussian process prediction for time series of structured data”. Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Ed. Michele Verleysen. Bruges, 2016. 41-46.
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