Learning Vector Quantization: generalization ability and dynamics of competing prototypes

Witoelar, Aree; Biehl, Michael; Hammer, Barbara

Learning Vector Quantization: generalization ability and dynamics of competing prototypes

Witoelar A, Biehl M, Hammer B (2007)
In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.

Konferenzbeitrag | Veröffentlicht | Englisch

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DOI

https://doi.org/10.2390/biecoll-wsom2007-126

URN

urn:nbn:de:0070-pub-19942956

Autor*in

Witoelar, Aree; Biehl, Michael; Hammer, Barbara^UniBi

Einrichtung

Technische Fakultät > AG Machine Learning

Abstract / Bemerkung

Learning Vector Quantization (LVQ) are popular multi-class classification algorithms. Prototypes in an LVQ system represent the typical features of classes in the data. Frequently multiple prototypes are employed for a class to improve the representation of variations within the class and the generalization ability. In this paper, we investigate the dynamics of LVQ in an exact mathematical way, aiming at understanding the influence of the number of prototypes and their assignment to classes. The theory of on-line learning allows a mathematical description of the learning dynamics in model situations. We demonstrate using a system of three prototypes the different behaviors of LVQ systems of multiple prototype and single prototype class representation.

Erscheinungsjahr

2007

Titel des Konferenzbandes

Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007)

Konferenz

WSOM 2007

Konferenzort

Bielefeld, Germany

Konferenzdatum

2007-09-03 – 2007-09-06

ISBN

978-3-00-022473-7

Page URI

https://pub.uni-bielefeld.de/record/1994295

Zitieren

Witoelar A, Biehl M, Hammer B. Learning Vector Quantization: generalization ability and dynamics of competing prototypes. In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University; 2007.

Witoelar, A., Biehl, M., & Hammer, B. (2007). Learning Vector Quantization: generalization ability and dynamics of competing prototypes. Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007) Bielefeld: Bielefeld University. https://doi.org/10.2390/biecoll-wsom2007-126

Witoelar, Aree, Biehl, Michael, and Hammer, Barbara. 2007. “Learning Vector Quantization: generalization ability and dynamics of competing prototypes”. In Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.

Witoelar, A., Biehl, M., and Hammer, B. (2007). “Learning Vector Quantization: generalization ability and dynamics of competing prototypes” in Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007) (Bielefeld: Bielefeld University).

Witoelar, A., Biehl, M., & Hammer, B., 2007. Learning Vector Quantization: generalization ability and dynamics of competing prototypes. In Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.

A. Witoelar, M. Biehl, and B. Hammer, “Learning Vector Quantization: generalization ability and dynamics of competing prototypes”, Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007), Bielefeld: Bielefeld University, 2007.

Witoelar, A., Biehl, M., Hammer, B.: Learning Vector Quantization: generalization ability and dynamics of competing prototypes. Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld University, Bielefeld (2007).

Witoelar, Aree, Biehl, Michael, and Hammer, Barbara. “Learning Vector Quantization: generalization ability and dynamics of competing prototypes”. Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University, 2007.

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