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Noise-injected neural networks show promise for use on small-sample expression data
BACKGROUND: Overfitting the data is a salient issue for classifier design in small-sample settings. This is why selecting a classifier from a constrained family of classifiers, ones that do not possess the potential to too finely partition the feature space, is typically preferable. But overfitting...
| Autores principales: | , , , |
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| Formato: | Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2006
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1524820/ https://www.ncbi.nlm.nih.gov/pubmed/16737545 http://dx.doi.org/10.1186/1471-2105-7-274 |