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.