Conference paper

Appeared in: ESANN 2005, 13th European Symposium on Artificial Neural Networks

Editor list: Verleysen, Michel

Publication year: 2005

Pages: 331-338

ISBN: 2-930307-05-6

URL: https://www.esann.org/sites/default/files/proceedings/legacy/es2005-109.pdf

Conference: 13th European Symposium on Artificial Neural Networks

Abstract: 

Generalized Relevance Learning Vector Quantization (GRLVQ) is combined with correlation-based similarity measures. These are derived from the Pearson correlation coefficient in order to replace the adaptive squared Euclidean distance which is typically used for GRLVQ. Patterns can thus be used without further preprocessing and compared in a manner invariant to data shifting and scaling transforms. High accuracies are demonstrated for a reference experiment of handwritten character recognition and good discrimination ability is shown for the detection of systematic differences between gene expression experiments.

Harvard Citation style: Strickert, M., Sreenivasulu, N., Weschke, W., Seiffert, U. and Villmann, T. (2005) Generalized Relevance LVQ with Correlation Measures for Biological Data, in Verleysen, M. (ed.) ESANN 2005, 13th European Symposium on Artificial Neural Networks. Evere: D-side. pp. 331-338. https://www.esann.org/sites/default/files/proceedings/legacy/es2005-109.pdf

APA Citation style: Strickert, M., Sreenivasulu, N., Weschke, W., Seiffert, U., & Villmann, T. (2005). Generalized Relevance LVQ with Correlation Measures for Biological Data. In Verleysen, M. (Ed.), ESANN 2005, 13th European Symposium on Artificial Neural Networks. (pp. 331-338). D-side. https://www.esann.org/sites/default/files/proceedings/legacy/es2005-109.pdf