<abstract xmlns="http://www.w3.org/1999/xhtml"><p>.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.</p></abstract>

.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.

Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization

Computational 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

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

.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...

Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization

Online veröffentlicht: 30. Mai 2014

Seitenbereich: 79 - 105

Eingereicht: 01. Jan. 2014

DOI: https://doi.org/10.2478/fcds-2014-0006

Schlüsselwörter
learning vector quantization, non-standard metrics, classification, classification certainty, statistics

© M. Kaden et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization

Online veröffentlicht: 30. Mai 2014

Seitenbereich: 79 - 105

Eingereicht: 01. Jan. 2014

DOI: https://doi.org/10.2478/fcds-2014-0006

Schlüsselwörterlearning vector quantization, non-standard metrics, classification, classification certainty, statistics

© M. Kaden et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
learning vector quantization, non-standard metrics, classification, classification certainty, statistics