@inproceedings{39126f136e5045139f327e04eaad9465,
title = "Computational neuro-modeling of visual memory: Multimodal imaging and analysis",
abstract = "The high dimensionality of functional magnetic resonance imaging (fMRI) data presents major challenges to fMRI pattern classification. Directly applying standard classifiers often results in overfitting or singularity, which limits the generalizability of the results. In this paper, we propose a {"}Doubly Regularized LOgistic Regression Algorithm{"} (DR LORA) which penalizes the voxels of the brain that are of no importance for the classification using the Alternating Direction Method of Multipliers (ADMM) and therefore alleviate this overfitting problem. Our algorithm was compared to other classification based algorithms such as Naive Bayes, Random forest and support vector machine. The results show clear performances for our algorithm.",
keywords = "Alternating Direction Method of Multipliers, Classification, LASSO, Logistic Regression, fMRI",
author = "Mohammed Elanbari and Nawel Nemmour and Othmane Bouhali and Reda Rawi and Ali Sheharyar and Halima Bensmail",
year = "2014",
doi = "10.1007/978-3-319-09891-3_3",
language = "English",
isbn = "9783319098906",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "21--32",
booktitle = "Brain Informatics and Health - International Conference, BIH 2014, Proceedings",
address = "Germany",
note = "2014 International Conference on Brain Informatics and Health, BIH 2014 ; Conference date: 11-08-2014 Through 14-08-2014",
}