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Relevance Learning in Unsupervised Vector Quantization Based on Divergences.

M. Kästner, A. Backhaus, T. Geweniger, S. Haase, U. Seiffert, and T. Villmann. WSOM, volume 6731 of Lecture Notes in Computer Science, page 90-100. Springer, (2011)

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To differ between persons with the same name, the academic degree and the title of an important publication will be displayed. You can also use the button next to the name to display some publications already assigned to the person.

Prof. Dr. Andreas Hotho

Ontology-based Text Document Clustering.A. Hotho, A. Maedche, and S. Staab. KI, 16 (4): 48-54 (2002)

Ontology-based Text Document Clustering.A. Hotho, A. Maedche, and S. Staab. KI, 16 (4): 48-54 (2002)Clustern mit Hintergrundwissen.A. Hotho. Karlsruhe Institute of Technology, (2004)

Junior-Prof. Dr.-Ing. Andreas Pott

Andreas Kaufmann

Andreas Pahler University of Stuttgart

Hon. -Prof. Dr. -Ing. Andreas Pott University of Stuttgart

Other publications of authors with the same name

Quantitative Measurements of model interpretability for the analysis of spectral data.A. Backhaus, and U. Seiffert. CIDM, page 18-25. IEEE, (2013)Phenoliner: A New Field Phenotyping Platform for Grapevine Research.A. Kicherer, K. Herzog, N. Bendel, H. Klück, A. Backhaus, M. Wieland, J. Rose, L. Klingbeil, T. Läbe, C. Hohl and 4 other author(s). Sensors, 17 (7): 1625 (2017)Relevance Learning in Unsupervised Vector Quantization Based on Divergences.M. Kästner, A. Backhaus, T. Geweniger, S. Haase, U. Seiffert, and T. Villmann. WSOM, volume 6731 of Lecture Notes in Computer Science, page 90-100. Springer, (2011)Contextual Learning in the Selective Attention for Identification model (CL-SAIM): Modeling contextual cueing in visual search tasks.A. Backhaus, D. Heinke, and G. Humphreys. CVPR Workshops, page 87. IEEE Computer Society, (2005)The Selective Attention for Identification Model (SAIM): Simulating Visual Search in Natural Colour Images.D. Heinke, A. Backhaus, Y. Sun, and G. Humphreys. WAPCV, volume 4840 of Lecture Notes in Computer Science, page 141-154. Springer, (2007)Classification in high-dimensional spectral data: Accuracy vs. interpretability vs. model size.A. Backhaus, and U. Seiffert. Neurocomputing, (2014)Fusion trees for fast and accurate classification of hyperspectral data with ensembles of γ-divergence-based RBF networks.U. Knauer, A. Backhaus, and U. Seiffert. Neural Computing and Applications, 26 (2): 253-262 (2015)Evaluation of Fusion Methods for Gamma-Divergence-Based Neural Network Ensembles.U. Knauer, A. Backhaus, and U. Seiffert. SSCI, page 322-327. IEEE, (2015)Validation of unsupervised clustering methods for leaf phenotype screening.A. Backhaus, A. Kuwabara, A. Fleming, and U. Seiffert. ESANN, (2010)Classifying Scotch Whisky from near-infrared Raman spectra with a Radial Basis Function Network with Relevance Learning.A. Backhaus, P. Ashok, B. Praveen, K. Dholakia, and U. Seiffert. ESANN, (2012)