56 search results - page 1 / 12 » Stochastic methods for l1 regularized loss minimization |
ICML
2009
IEEE
13 years 11 months ago
2009
IEEE
CORR
2011
Springer
12 years 8 months ago
2011
Springer
We propose Shotgun, a parallel coordinate descent algorithm for minimizing L1regularized losses. Though coordinate descent seems inherently sequential, we prove convergence bounds...
NIPS
2008
13 years 6 months ago
2008
The stochastic approximation method is behind the solution to many important, actively-studied problems in machine learning. Despite its farreaching application, there is almost n...
ECML
2007
Springer
13 years 11 months ago
2007
Springer
L1 regularization is effective for feature selection, but the resulting optimization is challenging due to the non-differentiability of the 1-norm. In this paper we compare state...
ACL
2009
13 years 2 months ago
2009
Stochastic gradient descent (SGD) uses approximate gradients estimated from subsets of the training data and updates the parameters in an online fashion. This learning framework i...