Abstract:

It has recently been shown that feature selection in
supervised learning can be embedded in the learning
algorithm by using sparsity-promoting priors/penalties
that encourage the coefficient estimates to be either
significantly large or exactly zero. In the first half
of this talk, I will review this type of approach (which
includes the well-known LASSO criterion for regression)
and present some recent developments: (i) simple and efficient
algorithms (with both parallel and sequential updates)
for both binary and multi-class problems; (ii) generalization
bounds; (iii) feature selection “inside” the kernel for
kernel-based formulations. Experimental results (on standard
benchmark data-sets and also on gene expression data)
reveal that this class of methods achieves state-of-the-art
performance.

Date: 2004-May-14     Time: 14:00:00     Room: 336

For more information:

• Mario.Figueiredo@lx.it.pt