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Genetic Algorithms for Finite Mixture Model Based Voxel Classification in Neuroimaging
Finite mixture models (FMMs) are an indispensable tool for unsupervised classification in brain imaging. Fitting an FMM to the data leads to a complex optimization problem. This optimization problem is difficult to solve by standard local optimization methods, such as the expectation-maximization (E...
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| Auteurs principaux: | , , , , , , |
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| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
2007
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| Sujets: | |
| Accès en ligne: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3192854/ https://ncbi.nlm.nih.gov/pubmed/17518064 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/TMI.2007.895453 |
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