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
Konferenzbeitrag | Veröffentlicht | Englisch
Herausgeber
Abstract / Bemerkung
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.
Erscheinungsjahr
Titel des Konferenzbandes
ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Seite
265-270
Konferenz
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Konferenzort
Bruges, Belgium
Konferenzdatum
2014-04-23 – 2014-04-25
PUB-ID

Zitieren

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, Verleysen, M. ed. (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.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Copyright Statement:
This Item is protected by copyright and/or related rights. [...]
Volltext(e)
Access Level
OA Open Access
Zuletzt Hochgeladen
2015-01-16T12:16:00Z

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Suchen in

Google Scholar