Supervised Neural Gas for Learning Vector Quantization

In this contribution we combine approaches the generalized leraning ve ctor quantization (GLVQ) with the neighborhood orientented learning in the neural gas netw ork (NG). In this way we obtain a supervised version of the NG what we call supervised NG (S ). We show that the SNG is more robust than the GLVQ because the neighborhood lear ning voids numerically instabilities as it may occur for complicate classifi cation tasks like in the case of multimodal data.