The paper deals with the concept of relevance learning in learning vector quantization and classification. Recent machine learning approaches with the ability of metric adaptation...
Thomas Villmann, Frank-Michael Schleif, Barbara Ha...
This paper describes an approach to robotic control that is patterned after models of human skill acquisition. The intent is to develop robots capable of learning how to accomplis...
We address the problem of classification in partially labeled networks (a.k.a. within-network classification) where observed class labels are sparse. Techniques for statistical re...
Brian Gallagher, Hanghang Tong, Tina Eliassi-Rad, ...
— The idea of using evolutionary techniques to optimize the performance of neural networks is now widely used, but some approaches have been found to result in the evolution of r...
We present a novel multi-layered unsupervised learning artifical immune system (MARIA). We have employed vector quantisation to augment MARIA (and Kohonen Networks) to allow for a ...