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Learning Interpretable SVMs for Biological Sequence Classification
BACKGROUND: Support Vector Machines (SVMs) – using a variety of string kernels – have been successfully applied to biological sequence classification problems. While SVMs achieve high classification accuracy they lack interpretability. In many applications, it does not suffice that an algorithm just...
| Autores principales: | , , |
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| Formato: | Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2006
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1810320/ https://www.ncbi.nlm.nih.gov/pubmed/16723012 http://dx.doi.org/10.1186/1471-2105-7-S1-S9 |