Adaptive distance measures for sequential data

Mokbel B, Paaßen B, Hammer B (2014)
In: ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed);Bruges, Belgium: i6doc.com: 265-270.

Download
OA
Conference Paper | Published | English
Editor
Verleysen, Michel
Abstract
Recent extensions of learning vector quantization (LVQ) to general (dis-)similarity data have paved the way towards LVQ classifiers for possibly discrete, structured objects such as sequences addressed by classical alignment. In this contribution, we propose a metric learning scheme based on this framework which allows for autonomous learning of the underlying scoring matrix according to a given discriminative task. Besides facilitating the often crucial and problematic choice of the scoring matrix in applications, this extension offers an increased interpretability of the results by pointing out structural invariances for the given task.
Publishing Year
Conference
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Location
Bruges, Belgium
Conference Date
2014-04-23 – 2014-04-25
PUB-ID

Cite this

Mokbel B, Paaßen B, Hammer B. Adaptive distance measures for sequential data. In: Verleysen M, ed. ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium: i6doc.com; 2014: 265-270.
Mokbel, B., Paaßen, B., & Hammer, B. (2014). Adaptive distance measures for sequential data. In M. Verleysen (Ed.), ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 265-270). Bruges, Belgium: i6doc.com.
Mokbel, B., Paaßen, B., and Hammer, B. (2014). “Adaptive distance measures for sequential data” in ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ed. M. Verleysen (Bruges, Belgium: i6doc.com), 265-270.
Mokbel, B., Paaßen, B., & Hammer, B., 2014. Adaptive distance measures for sequential data. In M. Verleysen, ed. ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium: i6doc.com, pp. 265-270.
B. Mokbel, B. Paaßen, and B. Hammer, “Adaptive distance measures for sequential data”, ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, M. Verleysen, ed., Bruges, Belgium: i6doc.com, 2014, pp.265-270.
Mokbel, B., Paaßen, B., Hammer, B.: Adaptive distance measures for sequential data. In: Verleysen, M. (ed.) ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. p. 265-270. i6doc.com, Bruges, Belgium (2014).
Mokbel, Bassam, Paaßen, Benjamin, and Hammer, Barbara. “Adaptive distance measures for sequential data”. ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Ed. Michel Verleysen. Bruges, Belgium: i6doc.com, 2014. 265-270.
Main File(s)
Access Level
OA Open Access
Last Uploaded
2015-01-16 12:16:00

This data publication is cited in the following publications:
This publication cites the following data publications:

Export

0 Marked Publications

Open Data PUB

Search this title in

Google Scholar