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Abstract
  • We present an architecture for incremental online learning in high-dimensional feature spaces and apply it on a mobile robot.
    The model is based on learning vector quantization, approaching the stability-plasticity problem of incremental learning by adaptive insertions
    of representative vectors.
    We employ a cost-function-based learning vector quantization approach and introduce a new insertion strategy optimizing a cost-function based on
    a subset of samples.
    We demonstrate this model within a real-time application for a mobile robot scenario, where we perform interactive real-time learning of visual categories.
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