267 search results - page 15 / 54 » Convex optimization for the design of learning machines |
ECML
2007
Springer
14 years 3 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...
JMLR
2010
13 years 3 months ago
2010
In recent years, a fundamental problem structure has emerged as very useful in a variety of machine learning applications: Submodularity is an intuitive diminishing returns proper...
IJCNN
2006
IEEE
14 years 3 months ago
2006
IEEE
— A key step in many statistical learning methods used in machine learning involves solving a convex optimization problem containing one or more hyper-parameters that must be sel...
PLDI
2003
ACM
14 years 2 months ago
2003
ACM
Compiler writers have crafted many heuristics over the years to approximately solve NP-hard problems efficiently. Finding a heuristic that performs well on a broad range of applic...
ICML
2009
IEEE
14 years 10 months ago
2009
IEEE
Recent work has shown that one can learn the structure of Gaussian Graphical Models by imposing an L1 penalty on the precision matrix, and then using efficient convex optimization...