Prototype‐based models in machine learning. Issue 2 (21st January 2016)

• Record Type: Journal Article
• Authors: Biehl, Michael; Hammer, Barbara; Villmann, Thomas
• Is Part Of: Wiley interdisciplinary reviews. Volume 7:Issue 2(2016:Mar./Apr.)
• Page Start: 92
• Page End: 111
• Publication Date: 2016-01-21
• Subjects: Cognitive science -- Periodicals; 153.05
• DOI: 10.1002/wcs.1378
• Languages: English
• ISSNs: 1939-5086

Abstract

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. WIREs Cogn Sci 2016, 7:92–111. doi: 10.1002/wcs.1378 This article is categorized under: Psychology > Development and Aging Psychology > Learning