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Quantitative Measurements of model interpretability for the analysis of spectral data., and . CIDM, page 18-25. IEEE, (2013)Phenoliner: A New Field Phenotyping Platform for Grapevine Research., , , , , , , , , and 4 other author(s). Sensors 17 (7): 1625 (2017)Relevance Learning in Unsupervised Vector Quantization Based on Divergences., , , , , and . 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., , and . CVPR Workshops, page 87. IEEE Computer Society, (2005)The Selective Attention for Identification Model (SAIM): Simulating Visual Search in Natural Colour Images., , , and . 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., and . Neurocomputing (2014)Fusion trees for fast and accurate classification of hyperspectral data with ensembles of γ-divergence-based RBF networks., , and . Neural Computing and Applications 26 (2): 253-262 (2015)Evaluation of Fusion Methods for Gamma-Divergence-Based Neural Network Ensembles., , and . SSCI, page 322-327. IEEE, (2015)Validation of unsupervised clustering methods for leaf phenotype screening., , , and . ESANN, (2010)Classifying Scotch Whisky from near-infrared Raman spectra with a Radial Basis Function Network with Relevance Learning., , , , and . ESANN, (2012)