Konferenzpaper
Autorenliste: Strickert, Marc; Sreenivasulu, Nese; Weschke, Winfriede; Seiffert, Udo; Villmann, Thomas
Erschienen in: ESANN 2005, 13th European Symposium on Artificial Neural Networks
Herausgeberliste: Verleysen, Michel
Jahr der Veröffentlichung: 2005
Seiten: 331-338
ISBN: 2-930307-05-6
URL: https://www.esann.org/sites/default/files/proceedings/legacy/es2005-109.pdf
Konferenz: 13th European Symposium on Artificial Neural Networks
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.
Abstract:
Zitierstile
Harvard-Zitierstil: 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-Zitierstil: 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
