Embedding anatomical or functional knowledge in whole-brain multiple kernel learning models
Pattern recognition models have been increasingly applied to neuroimaging data over the
last two decades. These applications have ranged from cognitive neuroscience to clinical …
last two decades. These applications have ranged from cognitive neuroscience to clinical …
Model sparsity and brain pattern interpretation of classification models in neuroimaging
Interest is increasing in applying discriminative multivariate analysis techniques to the
analysis of functional neuroimaging data. Model interpretation is of great importance in the …
analysis of functional neuroimaging data. Model interpretation is of great importance in the …
Q-mkl: Matrix-induced regularization in multi-kernel learning with applications to neuroimaging
Abstract Multiple Kernel Learning (MKL) generalizes SVMs to the setting where one
simultaneously trains a linear classifier and chooses an optimal combination of given base …
simultaneously trains a linear classifier and chooses an optimal combination of given base …
Localizing and comparing weight maps generated from linear kernel machine learning models
J Schrouff, J Cremers, G Garraux… - … Workshop on Pattern …, 2013 - ieeexplore.ieee.org
Recently, machine learning models have been applied to neuroimaging data, allowing to
make predictions about a variable of interest based on the pattern of activation or anatomy …
make predictions about a variable of interest based on the pattern of activation or anatomy …
Semi-supervised factored logistic regression for high-dimensional neuroimaging data
Imaging neuroscience links human behavior to aspects of brain biology in ever-increasing
datasets. Existing neuroimaging methods typically perform either discovery of unknown …
datasets. Existing neuroimaging methods typically perform either discovery of unknown …
[HTML][HTML] Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison …
Background Machine learning techniques such as support vector machine (SVM) have been
applied recently in order to accurately classify individuals with neuropsychiatric disorders …
applied recently in order to accurately classify individuals with neuropsychiatric disorders …
[HTML][HTML] Combining heterogeneous data sources for neuroimaging based diagnosis: re-weighting and selecting what is important
Combining neuroimaging and clinical information for diagnosis, as for example behavioral
tasks and genetics characteristics, is potentially beneficial but presents challenges in terms …
tasks and genetics characteristics, is potentially beneficial but presents challenges in terms …
[HTML][HTML] Bayesian multi-task learning for decoding multi-subject neuroimaging data
Decoding models based on pattern recognition (PR) are becoming increasingly important
tools for neuroimaging data analysis. In contrast to alternative (mass-univariate) encoding …
tools for neuroimaging data analysis. In contrast to alternative (mass-univariate) encoding …
Multi-way multi-level kernel modeling for neuroimaging classification
Owing to prominence as a diagnostic tool for probing the neural correlates of cognition,
neuroimaging tensor data has been the focus of intense investigation. Although many …
neuroimaging tensor data has been the focus of intense investigation. Although many …
Subject specific sparse dictionary learning for atlas based brain MRI segmentation
Quantitative measurements from segmentations of soft tissues from magnetic resonance
images (MRI) of human brains provide important biomarkers for normal aging, as well as …
images (MRI) of human brains provide important biomarkers for normal aging, as well as …