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.

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Conference Paper | Published | English
Editor
Wermter, Stefan ; Weber, Cornelius ; Duch, Włodzisław ; Honkela, Timo ; Koprinkova-Hristova, Petia ; Magg, Sven ; Palm, Günther ; Villa, Allessandro
Abstract
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.
Publishing Year
Conference
Artificial Neural Networks and Machine Learning - ICANN 2014
Location
Hamburg
Conference Date
2014-09-15 – 2014-09-19
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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.
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, ed. S. Wermter, C. Weber, W. Duch, T. Honkela, P. Koprinkova-Hristova, S. Magg, G. Palm, and A. Villa 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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