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ML
2007
ACM
144views Machine Learning» more  ML 2007»
15 years 3 months ago
Invariant kernel functions for pattern analysis and machine learning
In many learning problems prior knowledge about pattern variations can be formalized and beneficially incorporated into the analysis system. The corresponding notion of invarianc...
Bernard Haasdonk, Hans Burkhardt
115
Voted
CORR
2011
Springer
150views Education» more  CORR 2011»
14 years 10 months ago
Total variation regularization for fMRI-based prediction of behaviour
—While medical imaging typically provides massive amounts of data, the extraction of relevant information for predictive diagnosis remains a difficult challenge. Functional MRI ...
Vincent Michel, Alexandre Gramfort, Gaël Varo...
131
Voted
JMLR
2010
151views more  JMLR 2010»
14 years 10 months ago
Understanding the difficulty of training deep feedforward neural networks
Whereas before 2006 it appears that deep multilayer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with e...
Xavier Glorot, Yoshua Bengio
215
Voted
TIP
1998
456views more  TIP 1998»
13 years 11 months ago
Snakes, Shapes, and Gradient Vector Flow
Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initiali...
Chenyang Xu, Jerry L. Prince
149
Voted
IPMI
2003
Springer
16 years 4 months ago
Analysis of Event-Related fMRI Data Using Best Clustering Bases
We explore a new paradigm for the analysis of event-related functional magnetic resonance images (fMRI) of brain activity. We regard the fMRI data as a very large set of time serie...
François G. Meyer, Jatuporn Chinrungrueng