Prototype-based models in machine learning

Wiley Interdisciplinary Reviews. Cognitive Science
Michael BiehlThomas Villmann

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.

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Citations

Apr 17, 2020·Alimentary Pharmacology & Therapeutics·Ahmad MoollaJeremy W Tomlinson
Oct 10, 2018·Entropy·Michiel StraatMichael Biehl
Feb 18, 2021·Scientific Reports·Nobuhito ManomeUng-Il Chung
Feb 18, 2020··Michael BiehlErnest Mwebaze

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