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...

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Detalles Bibliográficos
Autores principales: Rätsch, Gunnar, Sonnenburg, Sören, Schäfer, Christin
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Proceedings
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

Internet

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

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