  We address in this paper the problem of multi-channel signal sequence
labeling. In particular, we consider the problem where the signals are
contaminated by noise or may present some dephasing with respect to their
labels. For that, we propose to jointly learn a SVM sample classifier with a
temporal filtering of the channels. This will lead to a large margin filtering
that is adapted to the specificity of each channel (noise and time-lag). We
derive algorithms to solve the optimization problem and we discuss different
filter regularizations for automated scaling or selection of channels. Our
approach is tested on a non-linear toy example and on a BCI dataset. Results
show that the classification performance on these problems can be improved by
learning a large margin filtering.
