Kernel Learning by Spectral Representation and Gaussian Mixtures
نویسندگان
چکیده
One of the main tasks in kernel methods is selection adequate mappings into higher dimension order to improve class classification. However, this tends be time consuming, and it may not finish with best separation between classes. Therefore, there a need for better that are able extract distance from data. This work presents novel approach learning such by using locally stationary kernels, spectral representations Gaussian mixtures.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2023
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app13042473