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); Louvain-la-Neuve: Ciaco - i6doc.com: 41--46.
Konferenzbeitrag
| Veröffentlicht | Englisch
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Herausgeber*in
Verleysen, Michele
Einrichtung
Abstract / Bemerkung
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.
Stichworte
structured data;
gaussian processes;
time series prediction
Erscheinungsjahr
2016
Titel des Konferenzbandes
Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Seite(n)
41--46
Konferenz
24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Konferenzort
Bruges
Konferenzdatum
2016-04-27 – 2016-04-29
ISBN
978-2-87587-026-1
Page URI
https://pub.uni-bielefeld.de/record/2900676
Zitieren
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. Louvain-la-Neuve: Ciaco - i6doc.com; 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). Louvain-la-Neuve: Ciaco - i6doc.com.
Paaßen, Benjamin, Göpfert, Christina, and Hammer, Barbara. 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. Michele Verleysen, 41--46. Louvain-la-Neuve: Ciaco - i6doc.com.
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, Verleysen, M. ed. (Louvain-la-Neuve: Ciaco - i6doc.com), 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. Louvain-la-Neuve: Ciaco - i6doc.com, 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., Louvain-la-Neuve: Ciaco - i6doc.com, 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. Ciaco - i6doc.com, Louvain-la-Neuve (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. Louvain-la-Neuve: Ciaco - i6doc.com, 2016. 41--46.
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