Prototype-Based Online Learning on Homogeneously Labeled Streaming Data
Limberg C, Göpfert JP, Wersing H, Ritter H (2020)
In: Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II. Farkaš I, Masulli P, Wermter S (Eds); Lecture Notes in Computer Science, 12397. Cham: Springer International Publishing: 204-213.
Konferenzbeitrag
| Veröffentlicht | Englisch
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Autor*in
Herausgeber*in
Farkaš, Igor;
Masulli, Paolo;
Wermter, Stefan
Einrichtung
Abstract / Bemerkung
Algorithms in machine learning commonly require training data to be independent and identically distributed. This assumption is not always valid, e. g. in online learning, when data becomes available in homogeneously labeled blocks, which can severely impede especially instance-based learning algorithms. In this work, we analyze and visualize this issue, and we propose and evaluate strategies for Learning Vector Quantization to compensate for homogeneously labeled blocks. We achieve considerably improved results in this difficult setting.
Erscheinungsjahr
2020
Titel des Konferenzbandes
Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II
Serien- oder Zeitschriftentitel
Lecture Notes in Computer Science
Band
12397
Seite(n)
204-213
Konferenz
29th International Conference on Artificial Neural Networks,
Konferenzort
Bratislava, Slovakia
Konferenzdatum
2020-09-15 – 2020-09-18
ISBN
978-3-030-61615-1
eISBN
978-3-030-61616-8
ISSN
0302-9743
eISSN
1611-3349
Page URI
https://pub.uni-bielefeld.de/record/2950094
Zitieren
Limberg C, Göpfert JP, Wersing H, Ritter H. Prototype-Based Online Learning on Homogeneously Labeled Streaming Data. In: Farkaš I, Masulli P, Wermter S, eds. Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II. Lecture Notes in Computer Science. Vol 12397. Cham: Springer International Publishing; 2020: 204-213.
Limberg, C., Göpfert, J. P., Wersing, H., & Ritter, H. (2020). Prototype-Based Online Learning on Homogeneously Labeled Streaming Data. In I. Farkaš, P. Masulli, & S. Wermter (Eds.), Lecture Notes in Computer Science: Vol. 12397. Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II (pp. 204-213). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-61616-8_17
Limberg, Christian, Göpfert, Jan Philip, Wersing, Heiko, and Ritter, Helge. 2020. “Prototype-Based Online Learning on Homogeneously Labeled Streaming Data”. In Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II, ed. Igor Farkaš, Paolo Masulli, and Stefan Wermter, 12397:204-213. Lecture Notes in Computer Science. Cham: Springer International Publishing.
Limberg, C., Göpfert, J. P., Wersing, H., and Ritter, H. (2020). “Prototype-Based Online Learning on Homogeneously Labeled Streaming Data” in Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II, Farkaš, I., Masulli, P., and Wermter, S. eds. Lecture Notes in Computer Science, vol. 12397, (Cham: Springer International Publishing), 204-213.
Limberg, C., et al., 2020. Prototype-Based Online Learning on Homogeneously Labeled Streaming Data. In I. Farkaš, P. Masulli, & S. Wermter, eds. Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II. Lecture Notes in Computer Science. no.12397 Cham: Springer International Publishing, pp. 204-213.
C. Limberg, et al., “Prototype-Based Online Learning on Homogeneously Labeled Streaming Data”, Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II, I. Farkaš, P. Masulli, and S. Wermter, eds., Lecture Notes in Computer Science, vol. 12397, Cham: Springer International Publishing, 2020, pp.204-213.
Limberg, C., Göpfert, J.P., Wersing, H., Ritter, H.: Prototype-Based Online Learning on Homogeneously Labeled Streaming Data. In: Farkaš, I., Masulli, P., and Wermter, S. (eds.) Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II. Lecture Notes in Computer Science. 12397, p. 204-213. Springer International Publishing, Cham (2020).
Limberg, Christian, Göpfert, Jan Philip, Wersing, Heiko, and Ritter, Helge. “Prototype-Based Online Learning on Homogeneously Labeled Streaming Data”. Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II. Ed. Igor Farkaš, Paolo Masulli, and Stefan Wermter. Cham: Springer International Publishing, 2020.Vol. 12397. Lecture Notes in Computer Science. 204-213.
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