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.
Konferenzbeitrag
| Veröffentlicht | Englisch
Herausgeber*in
Verleysen, Michel
Einrichtung
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.
Stichworte
learning vector quantization;
sequence alignment;
dissimilarity data;
metric adaptation;
metric learning
Erscheinungsjahr
2014
Titel des Konferenzbandes
ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Seite(n)
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
ISSN
9782874190957
Page URI
https://pub.uni-bielefeld.de/record/2673554
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, Bassam, Paaßen, Benjamin, and Hammer, Barbara. 2014. “Adaptive distance measures for sequential data”. In ESANN, 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ed. Michel Verleysen, 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.
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Access Level
Open Access
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