Efficient Adaptation of Structure Metrics in Prototype-Based Classification
Mokbel B, Paaßen B, Hammer B (2014)
In: Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings. Wermter S, Weber C, Duch W, Honkela T, Koprinkova-Hristova P, Magg S, Palm G, Villa A (Eds); Lecture Notes in Computer Science, 8681. Springer: 571-578.
Konferenzbeitrag
| Veröffentlicht | Englisch
Herausgeber*in
Wermter, Stefan;
Weber, Cornelius;
Duch, Włodzisław;
Honkela, Timo;
Koprinkova-Hristova, Petia;
Magg, Sven;
Palm, Günther;
Villa, Allessandro
Einrichtung
Abstract / Bemerkung
More complex data formats and dedicated structure metrics have spurred the development of intuitive machine learning techniques which directly deal with dissimilarity data, such as relational learning vector quantization (RLVQ). The adjustment of metric parameters like relevance weights for basic structural elements constitutes a crucial issue therein, and first methods to automatically learn metric parameters from given data were proposed recently. In this contribution, we investigate a robust learning scheme to adapt metric parameters such as the scoring matrix in sequence alignment in conjunction with prototype learning, and we investigate the suitability of efficient approximations thereof.
Stichworte
learning vector quantization sequence alignment dissimilarity data metric learning metric adaptation
Erscheinungsjahr
2014
Titel des Konferenzbandes
Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings
Serien- oder Zeitschriftentitel
Lecture Notes in Computer Science
Band
8681
Seite(n)
571-578
Konferenz
Artificial Neural Networks and Machine Learning - ICANN 2014
Konferenzort
Hamburg
Konferenzdatum
2014-09-15 – 2014-09-19
ISBN
978-3-319-11178-0
Page URI
https://pub.uni-bielefeld.de/record/2710067
Zitieren
Mokbel B, Paaßen B, Hammer B. Efficient Adaptation of Structure Metrics in Prototype-Based Classification. In: Wermter S, Weber C, Duch W, et al., eds. Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings. Lecture Notes in Computer Science. Vol 8681. Springer; 2014: 571-578.
Mokbel, B., Paaßen, B., & Hammer, B. (2014). Efficient Adaptation of Structure Metrics in Prototype-Based Classification. In S. Wermter, C. Weber, W. Duch, T. Honkela, P. Koprinkova-Hristova, S. Magg, G. Palm, et al. (Eds.), Lecture Notes in Computer Science: Vol. 8681. Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings (pp. 571-578). Springer. doi:10.1007/978-3-319-11179-7_72
Mokbel, Bassam, Paaßen, Benjamin, and Hammer, Barbara. 2014. “Efficient Adaptation of Structure Metrics in Prototype-Based Classification”. In Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings, ed. Stefan Wermter, Cornelius Weber, Włodzisław Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, and Allessandro Villa, 8681:571-578. Lecture Notes in Computer Science. Springer.
Mokbel, B., Paaßen, B., and Hammer, B. (2014). “Efficient Adaptation of Structure Metrics in Prototype-Based Classification” in Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings, Wermter, S., Weber, C., Duch, W., Honkela, T., Koprinkova-Hristova, P., Magg, S., Palm, G., and Villa, A. eds. Lecture Notes in Computer Science, vol. 8681, (Springer), 571-578.
Mokbel, B., Paaßen, B., & Hammer, B., 2014. Efficient Adaptation of Structure Metrics in Prototype-Based Classification. In S. Wermter, et al., eds. Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings. Lecture Notes in Computer Science. no.8681 Springer, pp. 571-578.
B. Mokbel, B. Paaßen, and B. Hammer, “Efficient Adaptation of Structure Metrics in Prototype-Based Classification”, Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings, S. Wermter, et al., eds., Lecture Notes in Computer Science, vol. 8681, Springer, 2014, pp.571-578.
Mokbel, B., Paaßen, B., Hammer, B.: Efficient Adaptation of Structure Metrics in Prototype-Based Classification. In: Wermter, S., Weber, C., Duch, W., Honkela, T., Koprinkova-Hristova, P., Magg, S., Palm, G., and Villa, A. (eds.) Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings. Lecture Notes in Computer Science. 8681, p. 571-578. Springer (2014).
Mokbel, Bassam, Paaßen, Benjamin, and Hammer, Barbara. “Efficient Adaptation of Structure Metrics in Prototype-Based Classification”. Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings. Ed. Stefan Wermter, Cornelius Weber, Włodzisław Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, and Allessandro Villa. Springer, 2014.Vol. 8681. Lecture Notes in Computer Science. 571-578.
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