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.
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Abstract
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.
