Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization
.Classification is one of the most frequent tasks in machine learning. However, the variety of classification tasks as well as classifier methods is huge. Thus the question is coming up: which classifier is suitable for a given problem or how can we utilize a certain classifier model for different t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Sciendo
2014-05-01
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| Series: | Foundations of Computing and Decision Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/fcds-2014-0006 |
| _version_ | 1811321176392204288 |
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| author | Kaden M. Lange M. Nebel D. Riedel M. Geweniger T. Villmann T. |
| author_facet | Kaden M. Lange M. Nebel D. Riedel M. Geweniger T. Villmann T. |
| author_sort | Kaden M. |
| collection | DOAJ |
| description | .Classification is one of the most frequent tasks in machine learning. However, the variety of classification tasks as well as classifier methods is huge. Thus the question is coming up: which classifier is suitable for a given problem or how can we utilize a certain classifier model for different tasks in classification learning. This paper focuses on learning vector quantization classifiers as one of the most intuitive prototype based classification models. Recent extensions and modifications of the basic learning vector quantization algorithm, which are proposed in the last years, are highlighted and also discussed in relation to particular classification task scenarios like imbalanced and/or incomplete data, prior data knowledge, classification guarantees or adaptive data metrics for optimal classification. |
| first_indexed | 2024-04-13T13:13:28Z |
| format | Article |
| id | doaj.art-e0c60cfb21df414eba580fdb0c85a64a |
| institution | Directory Open Access Journal |
| issn | 2300-3405 |
| language | English |
| last_indexed | 2024-04-13T13:13:28Z |
| publishDate | 2014-05-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Foundations of Computing and Decision Sciences |
| spelling | doaj.art-e0c60cfb21df414eba580fdb0c85a64a2022-12-22T02:45:33ZengSciendoFoundations of Computing and Decision Sciences2300-34052014-05-013927910510.2478/fcds-2014-0006fcds-2014-0006Aspects in Classification Learning - Review of Recent Developments in Learning Vector QuantizationKaden M.Lange M.Nebel D.Riedel M.Geweniger T.Villmann T.0Computational Intelligence Group at the University of Applied Sciences Mittweida, Dept. of Mathematics, Technikumplatz 17, 09648 Mittweida, Saxonia - Germany, www: https://www.mni.hs-mittweida.de/webs/villmann.html.Classification is one of the most frequent tasks in machine learning. However, the variety of classification tasks as well as classifier methods is huge. Thus the question is coming up: which classifier is suitable for a given problem or how can we utilize a certain classifier model for different tasks in classification learning. This paper focuses on learning vector quantization classifiers as one of the most intuitive prototype based classification models. Recent extensions and modifications of the basic learning vector quantization algorithm, which are proposed in the last years, are highlighted and also discussed in relation to particular classification task scenarios like imbalanced and/or incomplete data, prior data knowledge, classification guarantees or adaptive data metrics for optimal classification.https://doi.org/10.2478/fcds-2014-0006learning vector quantizationnon-standard metricsclassificationclassification certaintystatistics |
| spellingShingle | Kaden M. Lange M. Nebel D. Riedel M. Geweniger T. Villmann T. Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization Foundations of Computing and Decision Sciences learning vector quantization non-standard metrics classification classification certainty statistics |
| title | Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization |
| title_full | Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization |
| title_fullStr | Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization |
| title_full_unstemmed | Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization |
| title_short | Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization |
| title_sort | aspects in classification learning review of recent developments in learning vector quantization |
| topic | learning vector quantization non-standard metrics classification classification certainty statistics |
| url | https://doi.org/10.2478/fcds-2014-0006 |
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