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Backhaus, Andreas

IPLodB ID: d3acc85b-47fb-47cc-961a-22685293bc94
Date Aligned: 09. 01. 2021
Version: 130

Collected from entities:

  • SciGraph Author: Backhaus, Andreas
    ID: sg:person.012005471271.36
    Last name: Backhaus First name: Andreas Gender: Male Activity years between: 2007 and 2015 * Similarities through collaboration * Coauthors: geweniger, tina haase, sven heinke, dietmar humphreys, glyn w. kastner, marika knauer, uwe seiffert, udo sun, yarou villmann, t. * Similarities through location * Springer Affiliations: biosystems engineering group, fraunhofer inst. f. fabrikbetrieb und -automatisierung iff, 39106, magdeburg, germany behavioural and brain sciences centre, university of birmingham, birmingham b15 2tt, united kingdom fraunhofer iff, biosystems engineering, 39106 magdeburg, germany biosystems engineering group, fraunhofer inst. f. fabrikbetrieb und -automatisierung iff, 39106 magdeburg, germany fraunhofer iff, joseph-v.-fraunhofer-str. 1, 39106 magdeburg, germany fraunhofer iff, sandtorstr. 22, 39106, magdeburg, germany fraunhofer institute for factory operation and automation university of birmingham EPO NUTS: DEE07 UKG31 Countries: GERMANY UNITED KINGDOM Cities: magdeburg birmingham Geocodes: 2874545 2655603 * Similarities through content * Journals: cognitive computation neural computing and applications Publications: advances in self-organizing maps advances in self-organizing maps and learning vector quantization attention in cognitive systems. theories and systems from an interdisciplinary viewpoint beyond standard metrics – on the selection and combination of distance metrics for an improved classification of hyperspectral data fusion trees for fast and accurate classification of hyperspectral data with ensembles of γ-divergence-based rbf networks modelling visual search with the selective attention for identification model (vs-saim): a novel explanation for visual search asymmetries relevance learning in unsupervised vector quantization based on divergences the selective attention for identification model (saim): simulating visual search in natural colour images Keywords: hyperspectral data identification model multiple object scenes selective attention