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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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
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