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Together with a suitable, discriminative distance or dissimilarity measure, prototypes can be used for the classification of complex, possibly high-dimensional data. We illustrate the framework in terms of the popular Learning Vector Quantization (LVQ). Most frequently, standard Euclidean distance is employed as a distance measure. We discuss how LVQ can be equipped with more general dissimilarites. Moreover, we introduce relevance learning as a tool for the data-driven optimization of parameterized distances.<\/jats:p>","DOI":"10.1017\/s1743921316012928","type":"journal-article","created":{"date-parts":[[2017,5,30]],"date-time":"2017-05-30T07:04:51Z","timestamp":1496127891000},"page":"129-138","source":"Crossref","is-referenced-by-count":1,"title":["Prototype-based Models for the Supervised Learning of Classification Schemes"],"prefix":"10.1017","volume":"12","author":[{"given":"Michael","family":"Biehl","sequence":"first","affiliation":[]},{"given":"Barbara","family":"Hammer","sequence":"additional","affiliation":[]},{"given":"Thomas","family":"Villmann","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2017,5,30]]},"reference":[{"key":"S1743921316012928_ref041","unstructured":"Biehl M. 2014, website: http:\/\/www.cs.rug.nl\/~biehl\/gmlvq"},{"key":"S1743921316012928_ref030","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2010.2042729"},{"key":"S1743921316012928_ref035","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1136\/annrheumdis-2014-206921","volume":"75","author":"Leo","year":"2016","journal-title":"Ann. of the Rheumatic Disease"},{"key":"S1743921316012928_ref026","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2010.10.016"},{"key":"S1743921316012928_ref032","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0059401"},{"key":"S1743921316012928_ref039","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2014.2377750"},{"key":"S1743921316012928_ref013","first-page":"423","volume-title":"Proc. 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