Creating and sharing knowledge for telecommunications

Online Learning of Structured Predictors with Multiple Kernels

Martins, A. ; Figueiredo, M. A. T. ; Aguiar, P. ; Smith, N. A. ; Xing, E. P.

Online Learning of Structured Predictors with Multiple Kernels, Proc International Conf. on Artificial Intelligence and Statistics - AISTATS, Fort Lauderdale, United States, Vol. -, pp. - - -, April, 2011.

Digital Object Identifier:

Download Full text PDF ( 634 KBs)


Training structured predictors often requires a
considerable time selecting features or tweak-
ing the kernel. Multiple kernel learning (MKL)
sidesteps this issue by embedding the kernel
learning into the training procedure. Despite the
recent progress towards efficiency and scalability
of MKL algorithms, the structured output case
remains an open research front. We propose a
new family of online proximal algorithms able to
tackle many variants of MKL and group-LASSO,
and for which we show regret, convergence, and
generalization bounds. Experiments on hand-
writing recognition and dependency parsing il-
lustrate the success of the approach.