title: Prototype-based models in machine learning.
abstract: An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of potentially high-dimensional, complex datasets. We discuss basic schemes of competitive vector quantization as well as the so-called neural gas approach and Kohonen's topology-preserving self-organizing map. Supervised learning in prototype systems is exemplified in terms of learning vector quantization. Most frequently, the familiar Euclidean distance serves as a dissimilarity measure. We present extensions of the framework to nonstandard measures and give an introduction to the use of adaptive distances in relevance learning.
authors.affiliation: Johann Bernoulli Institute for Mathematics and Computer Science, Faculty of Mathematics and Natural Sciences, University of Groningen, Groningen, The Netherlands.
authors.forename: Michael
authors.initials: M
authors.lastname: Biehl
authors.affiliation: CITEC Centre of Excellence, Faculty of Technology, Bielefeld University, Bielefeld, Germany.
authors.forename: Barbara
authors.initials: B
authors.lastname: Hammer
authors.affiliation: Department of Mathematics, University of Applied Sciences Mittweida, Mittweida, Germany.
authors.forename: Thomas
authors.initials: T
authors.lastname: Villmann
mesh_terms: D003198:Computer Simulation; D057225:Data Mining / Q000379:methods*; D000069550:Machine Learning; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D013223:Statistics as Topic