Border sensitive fuzzy vector quantization in semi-supervised learning

Abstract. We propose a semi-supervised fuzzy vector quantization method for the classification of incompletely labeled data. Since information contained within the structure of the data set should not be neglected, our method considers the whole data set during the learning process. In difference to known methods our approach uses neighborhood cooperativeness for stable prototype learning known from Neural Gas. Further improvement of the classification accuracy is achieved by including class border sensitivity inspired by Support Vector Machines again improved by neighborhood learning.