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Deep Low-Density Separation for Semi-supervised Classification
Given a small set of labeled data and a large set of unlabeled data, semi-supervised learning (ssl) attempts to leverage the location of the unlabeled datapoints in order to create a better classifier than could be obtained from supervised methods applied to the labeled training set alone. Effective...
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| Pubblicato in: | Computational Science – ICCS 2020 |
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| Autori principali: | , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
2020
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7304056/ https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1007/978-3-030-50420-5_22 |
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