This paper proposes a framework to learn concepts from di erent kinds of observations. We de ne a language to describe meta-concepts, that represent the sets of possible concepts ...
Automating the construction of semantic grammars is a di cult and interesting problem for machine learning. This paper shows how the semantic-grammar acquisition problem can be vi...
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...
— Within the present paper, we put forward a novel hybridization between support vector machines and evolutionary algorithms. Evolutionary support vector machines consider the cl...
Ruxandra Stoean, Mike Preuss, Catalin Stoean, Dumi...
Background: Multicategory Support Vector Machines (MC-SVM) are powerful classification systems with excellent performance in a variety of data classification problems. Since the p...