Konferenzpaper

Erschienen in: MIWOCI 2011, Mittweida Workshop on Computational Intelligence

Herausgeberliste: Schleif, Frank-Michael; Villmann, Thomas

Jahr der Veröffentlichung: 2011

Seiten: 5-15

URL: https://www.techfak.uni-bielefeld.de/~fschleif/mlr/mlr_06_2011.pdf

Konferenz: 3rd Mittweida Workshop on Computational Intelligence

Serientitel: Machine Learning Reports

Serienzählung: 2011, 06

Abstract: 

By combining very steep squashing functions and normalization as parts of the cost function of generalized learning vector quantization (GLVQ) and its descendants, vector label misclassification gets directly minimized in the limit of classseparating sigmoids towards step functions. To cope with the resulting difficult optimization problem a switch from standard stochastic gradient descent to a quasi- 2nd order Newton batch optimization scheme is proposed. Results for weighted squared Euclidean distance (GRLVQ) and adaptive matrix metrics (MRLVQ) are faster obtained and usually show a better class discrimination than traditional implementations of GRLVQ and MRLVQ. Code is available online.

Harvard-Zitierstil: Strickert, M. (2011) Enhancing M|G|RLVQ by quasi step discriminatory functions using 2nd order training, in Schleif, F. and Villmann, T. (eds.) MIWOCI 2011, Mittweida Workshop on Computational Intelligence. Mittweida: University of Applied Sciences. pp. 5-15. https://www.techfak.uni-bielefeld.de/~fschleif/mlr/mlr_06_2011.pdf

APA-Zitierstil: Strickert, M. (2011). Enhancing M|G|RLVQ by quasi step discriminatory functions using 2nd order training. In Schleif, F., & Villmann, T. (Eds.), MIWOCI 2011, Mittweida Workshop on Computational Intelligence. (pp. 5-15). University of Applied Sciences. https://www.techfak.uni-bielefeld.de/~fschleif/mlr/mlr_06_2011.pdf