Machine learning for neuroimaging with scikit-learn - Frontiers
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In this paper we have illustrated with simple examples how machine learning techniques can be applied to fMRI data using the scikit-learn Python toolkit in ...
Feb 21, 2014 · Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging ...
Dec 12, 2014 · Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging ...
In this paper we have illustrated with simple examples how machine learning techniques can be applied to fMRI data using the scikit-learn Python toolkit in ...
Feb 21, 2014 · This paper aims to fill the gap between machine learning and neuroimaging by demonstrating how a general-purpose machine-learning toolbox, ...
Oct 22, 2024 · Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging ...
It is illustrated how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps and its application to neuroimaging ...
Scikit-learn provides well-organized, high-quality tools for virtually all aspects of the typical machine learning workflow, including data loading and ...
Abstract: Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model ...
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Nilearn comes with code to simplify the use of scikit-learn when dealing with neuroimaging data. For the moment, nilearn is focused on functional MRI data.