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Gaussian bandwidth selection for manifold learning and classification
Kernel methods play a critical role in many machine learning algorithms. They are useful in manifold learning, classification, clustering and other data analysis tasks. Setting the kernel’s scale parameter, also referred to as the kernel’s bandwidth, highly affects the performance of the task in han...
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| Publicado no: | Data Min Knowl Discov |
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| Main Authors: | , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Springer US
2020
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7330274/ https://ncbi.nlm.nih.gov/pubmed/32837252 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/s10618-020-00692-x |
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