Prototype-based Models for the Supervised Learning of Classification Schemes. Issue Volume 12:Issue S325(2016) (30th May 2017)

• Record Type: Journal Article
• Authors: Biehl, Michael; Hammer, Barbara; Villmann, Thomas
• Editors: Brescia, M.; Djorgovski, S.G.; Feigelson, E.; Longo, G.; Cavuoti, S.
• Is Part Of: Proceedings of the International Astronomical Union. Volume 12:Issue S325(2016)
• Page Start: 129
• Page End: 138
• Publication Date: 2017-05-30
• Subjects: miscellaneous, -- methods: data analysis, -- techniques: miscellaneous; Astronomy -- Congresses; Astronomy -- Periodicals; 520
• DOI: 10.1017/S1743921316012928
• Languages: English
• ISSNs: 1743-9213

Abstract

Abstract: An introduction is given to the use of prototype-based models in supervised machine learning. The main concept of the framework is to represent previously observed data in terms of so-called prototypes, which reflect typical properties of the data. 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.