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Hierarchical Clustering of Sensorimotor Features

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KI 2009: Advances in Artificial Intelligence (KI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5803))

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Abstract

In this paper a method for clustering patterns represented by sets of sensorimotor features is introduced. Sensorimotor features as a biologically inspired representation have proofed to be working for the recognition task, but a method for unsupervised learning of classes from a set of patterns has been missing yet. By utilization of Self-Organizing Maps as a intermediate step, a hierarchy can be build with standard agglomerative clustering methods.

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Author information

Authors and Affiliations

  1. University of Bremen, 28359, Bremen, Germany

    Konrad Gadzicki

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  1. Konrad Gadzicki
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Editors and Affiliations

  1. GET Lab, University of Paderborn, Pohlweg 47-49, 33098, Paderborn, Germany

    Bärbel Mertsching , Marcus Hund  & Zaheer Aziz ,  & 

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Gadzicki, K. (2009). Hierarchical Clustering of Sensorimotor Features. In: Mertsching, B., Hund, M., Aziz, Z. (eds) KI 2009: Advances in Artificial Intelligence. KI 2009. Lecture Notes in Computer Science(), vol 5803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04617-9_42

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  • DOI: https://doi.org/10.1007/978-3-642-04617-9_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04616-2

  • Online ISBN: 978-3-642-04617-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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