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A Study of Domain Adaptation Classifiers Derived from Logistic Regression for the Task of Splice Site Prediction
Supervised classifiers are highly dependent on abundant labeled training data. Alternatives for addressing the lack of labeled data include: labeling data (but this is costly and time consuming); training classifiers with abundant data from another domain (however, the classification accuracy usuall...
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| Publicado no: | IEEE Trans Nanobioscience |
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| Main Authors: | , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
2016
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4894847/ https://ncbi.nlm.nih.gov/pubmed/26849871 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/TNB.2016.2522400 |
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