7 search results - page 1 / 2 » Generalized Sparse Regularization with Application to fMRI B... |
IPMI
2011
Springer
12 years 8 months ago
2011
Springer
Many current medical image analysis problems involve learning thousands or even millions of model parameters from extremely few samples. Employing sparse models provides an effecti...
CVPR
2011
IEEE
12 years 8 months ago
2011
IEEE
The perplexing effects of noise and high feature dimensionality greatly complicate functional magnetic resonance imaging (fMRI) classification. In this paper, we present a novel f...
MICCAI
2010
Springer
13 years 3 months ago
2010
Springer
The high dimensionality of functional magnetic resonance imaging (fMRI) data presents major challenges to fMRI pattern classification. Directly applying standard classifiers often ...
ICASSP
2009
IEEE
13 years 11 months ago
2009
IEEE
Recently proposed l1-regularized maximum-likelihood optimization methods for learning sparse Markov networks result into convex problems that can be solved optimally and efficien...
BRAIN
2010
Springer
13 years 3 months ago
2010
Springer
Discovering brain mechanisms underlying pain perception remains a challenging neuroscientific problem with important practical applications, such as developing better treatments f...