Efficient Accuracy Estimation for Instance-Based Incremental Active Learning

Limberg C, Wersing H, Ritter H (2018)
In: ESANN 2018. Proceedings., 26. Louvain-la-Neuve: i6doc: 171-176.

Konferenzbeitrag | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
Estimating system’s accuracy is crucial for applications of in- cremental learning. In this paper, we introduce the Distogram Estimation (DGE) approach to estimate the accuracy of instance-based classifiers. By calculating relative distances to samples it is possible to train an offline regression model, capable of predicting the classifier’s accuracy on unseen data. Our approach requires only a few supervised samples for training and can instantaneously be applied on unseen data afterwards. We evaluate our method on five benchmark data sets and for a robot object recognition task. Our algorithm clearly outperforms two baseline methods both for random and active selection of incremental training examples.
Stichworte
accuracy estimation, classifier, active learning
Erscheinungsjahr
2018
Titel des Konferenzbandes
ESANN 2018. Proceedings
Band
26
Seite(n)
171-176
Konferenz
26th European Symposium on Artificial Neural Networks (ESANN)
Konferenzort
Bruges
Konferenzdatum
2018-04-25 – 2018-04-27
ISBN
978-2-8758-7047-6
eISBN
978-2-8758-7048-3
Page URI
https://pub.uni-bielefeld.de/record/2920497

Zitieren

Limberg C, Wersing H, Ritter H. Efficient Accuracy Estimation for Instance-Based Incremental Active Learning. In: ESANN 2018. Proceedings. Vol 26. Louvain-la-Neuve: i6doc; 2018: 171-176.
Limberg, C., Wersing, H., & Ritter, H. (2018). Efficient Accuracy Estimation for Instance-Based Incremental Active Learning. ESANN 2018. Proceedings, 26, 171-176. Louvain-la-Neuve: i6doc.
Limberg, Christian, Wersing, Heiko, and Ritter, Helge. 2018. “Efficient Accuracy Estimation for Instance-Based Incremental Active Learning”. In ESANN 2018. Proceedings, 26:171-176. Louvain-la-Neuve: i6doc.
Limberg, C., Wersing, H., and Ritter, H. (2018). “Efficient Accuracy Estimation for Instance-Based Incremental Active Learning” in ESANN 2018. Proceedings, vol. 26, (Louvain-la-Neuve: i6doc), 171-176.
Limberg, C., Wersing, H., & Ritter, H., 2018. Efficient Accuracy Estimation for Instance-Based Incremental Active Learning. In ESANN 2018. Proceedings. no.26 Louvain-la-Neuve: i6doc, pp. 171-176.
C. Limberg, H. Wersing, and H. Ritter, “Efficient Accuracy Estimation for Instance-Based Incremental Active Learning”, ESANN 2018. Proceedings, vol. 26, Louvain-la-Neuve: i6doc, 2018, pp.171-176.
Limberg, C., Wersing, H., Ritter, H.: Efficient Accuracy Estimation for Instance-Based Incremental Active Learning. ESANN 2018. Proceedings. 26, p. 171-176. i6doc, Louvain-la-Neuve (2018).
Limberg, Christian, Wersing, Heiko, and Ritter, Helge. “Efficient Accuracy Estimation for Instance-Based Incremental Active Learning”. ESANN 2018. Proceedings. Louvain-la-Neuve: i6doc, 2018.Vol. 26. 171-176.
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