Admire LVQ—Adaptive Distance Measures in Relevance Learning Vector Quantization
| dc.contributor.author | Biehl, Michael | |
| dc.date.accessioned | 2018-01-08T09:16:10Z | |
| dc.date.available | 2018-01-08T09:16:10Z | |
| dc.date.issued | 2012 | |
| dc.description.abstract | The extension of Learning Vector Quantization by Matrix Relevance Learning is presented and discussed. The basic concept, essential properties, and several modifications of the scheme are outlined. A particularly successful application in the context of tumor classification highlights the usefulness and interpretability of the method in practical contexts. The development and putting forward of Matrix Relevance Learning Vector Quantization was, to a large extent, pursued in the frame of the project Adaptive Distance Measures in Relevance Learning Vector Quantization—Admire LVQ, funded through the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) under project code 612.066.620, from 2007 to 2011. | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11327 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 26, No. 4 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Adaptive distances | |
| dc.subject | Machine learning | |
| dc.subject | Prototype-based classification | |
| dc.subject | Similarity-based clustering | |
| dc.title | Admire LVQ—Adaptive Distance Measures in Relevance Learning Vector Quantization | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 395 | |
| gi.citation.startPage | 391 |