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Predicting the Critical Number of Layers for Hierarchical Support Vector Regression
Hierarchical support vector regression (HSVR) models a function from data as a linear combination of SVR models at a range of scales, starting at a coarse scale and moving to finer scales as the hierarchy continues. In the original formulation of HSVR, there were no rules for choosing the depth of t...
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| Pubblicato in: | Entropy (Basel) |
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| Autori principali: | , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
MDPI
2020
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7824529/ https://ncbi.nlm.nih.gov/pubmed/33383907 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/e23010037 |
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