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

Kaden, M; Lange, M.; Nebel, D; Riedel, M; Geweniger, T; Villmann, T.

doi:10.2478/fcds-2014-0006

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Tytuł artykułu

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

Autorzy

Kaden M , Lange M. , Nebel D , Riedel M , Geweniger T , Villmann T.

Wybrane pełne teksty z tego czasopisma

Identyfikatory

DOI

10.2478/fcds-2014-0006

Warianty tytułu

Języki publikacji

Abstrakty

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.

Słowa kluczowe

learning vector quantization non-standard metrics classification classification certainty statistics

Wydawca

Wydawnictwo Politechniki Poznańskiej

Czasopismo

Foundations of Computing and Decision Sciences

Rocznik

2014

Tom

Vol. 39, No. 2

Strony

79--105

Opis fizyczny

Bibliogr. 112 poz.

Twórcy

autor

Kaden M

Computational Intelligence Group at the University of Applied Sciences Mittweida, Dept. of Mathematics, Technikumplatz 17, 09648 Mittweida, Saxonia - Germany

autor

Lange M.

Computational Intelligence Group at the University of Applied Sciences Mittweida, Dept. of Mathematics, Technikumplatz 17, 09648 Mittweida, Saxonia - Germany

autor

Nebel D

Computational Intelligence Group at the University of Applied Sciences Mittweida, Dept. of Mathematics, Technikumplatz 17, 09648 Mittweida, Saxonia - Germany

autor

Riedel M

Computational Intelligence Group at the University of Applied Sciences Mittweida, Dept. of Mathematics, Technikumplatz 17, 09648 Mittweida, Saxonia - Germany

autor

Geweniger T

Computational Intelligence Group at the University of Applied Sciences Mittweida, Dept. of Mathematics, Technikumplatz 17, 09648 Mittweida, Saxonia - Germany

autor

Villmann T.

Computational Intelligence Group at the University of Applied Sciences Mittweida, Dept. of Mathematics, Technikumplatz 17, 09648 Mittweida, Saxonia - Germany

Bibliografia

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Bibliografia

Identyfikator YADDA

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