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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...
Gorde:
| Egile Nagusiak: | , , |
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| Formatua: | Artigo |
| Hizkuntza: | Inglês |
| Argitaratua: |
BioMed Central
2006
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| Gaiak: | |
| Sarrera elektronikoa: | https://ncbi.nlm.nih.gov/pmc/articles/PMC1810320/ https://ncbi.nlm.nih.gov/pubmed/16723012 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/1471-2105-7-S1-S9 |
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