title: Comparison of relevance learning vector quantization with other metric adaptive classification methods.
abstract: 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 but based on different concepts are considered in comparison to variants of relevance learning vector quantization. We compare these methods with respect to their theoretical motivation and we demonstrate the differences of their behavior for several real world data sets.
authors.affiliation: Clinic for Psychotherapy, University Leipzig, Karl-Tauchnitz-Str. 25, 04107 Leipzig, Germany. villmann@informatik.uni-leipzig.de
authors.forename: Th
authors.initials: T
authors.lastname: Villmann
authors.forename: F
authors.initials: F
authors.lastname: Schleif
authors.forename: B
authors.initials: B
authors.lastname: Hammer
mesh_terms: D001185:Artificial Intelligence; D006801:Humans; D007257:Information Theory; D007858:Learning / Q000145:classification / Q000502:physiology*; D009483:Neuropsychological Tests; D010363:Pattern Recognition, Automated / Q000379:methods*; D011237:Predictive Value of Tests