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The extension of the largest generalized-eigenvalue based distance metric D(ij)(γ(1)) in arbitrary feature spaces to classify composite data points
Analyzing patterns in data points embedded in linear and non-linear feature spaces is considered as one of the common research problems among different research areas, for example: data mining, machine learning, pattern recognition, and multivariate analysis. In this paper, data points are heterogen...
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Korea Genome Organization
2019
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6944050/ https://www.ncbi.nlm.nih.gov/pubmed/31896239 http://dx.doi.org/10.5808/GI.2019.17.4.e39 |