memberships in the highest level. The K-winner machine makes use of supervised and unsupervised learning
techniques. The hierarchical approach allows us to improve the classification speed during the application stage
and at the same time finely partitions the pattern space in the highest levels. In order to further improve the
performance of the hierarchical K-winner machine, we introduce the fuzzy membership assignment to the
prototype vectors. The developed system will be tested on some standard datasets and its performance will be
compared against competing approaches.
