Abstract
The paper deals with the application of the novel generalized relevance learning vector quantization based classification algorithm in comparison to the unified maximum separability analysis as a special variant of support vector machine algorithms. The algorithms are compared and their performance on real life data, taken from clinical studies, is demonstrated. It is shown that the vector quantization classifier gives competitive results in comparison to the considered support vector machine algorithm and shows the recently theoretical proven equivalence in classification capability of both paradigms.
Keywords
Relevance vector machineLearning vector quantizationVector quantizationSupport vector machineComputer scienceArtificial intelligenceEquivalence (formal languages)Pattern recognition (psychology)Relevance (law)Classifier (UML)Linde–Buzo–Gray algorithmMachine learningStructured support vector machineAlgorithmData miningMathematics
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Publication Info
- Year
- 2005
- Type
- article
- Volume
- 1
- Pages
- 374-379
- Citations
- 9
- Access
- Closed
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Cite This
Frank-Michael Schleif,
U. Clauss,
T. Villmann
et al.
(2005).
Supervised relevance neural gas and unified maximum separability analysis for classification of mass spectrometric data.
, 1
, 374-379.
https://doi.org/10.1109/icmla.2004.1383538
Identifiers
- DOI
- 10.1109/icmla.2004.1383538