JMLR
2012
12 years 1 months ago
2012
“Energy” models for continuous domains can be applied to many problems, but often suffer from high computational expense in training, due to the need to repeatedly minimize t...
SIGMOD
2012
ACM
12 years 1 months ago
2012
ACM
The success of data-driven solutions to difficult problems, along with the dropping costs of storing and processing massive amounts of data, has led to growing interest in largesc...
100
click to vote
EMMCVPR
2011
Springer
12 years 11 months ago
2011
Springer
Abstract. The Mumford-Shah model has been one of the most powerful models in image segmentation and denoising. The optimization of the multiphase Mumford-Shah energy functional has...
CIKM
2011
Springer
12 years 11 months ago
2011
Springer
Traditional feature selection methods assume that the data are independent and identically distributed (i.i.d.). In real world, tremendous amounts of data are distributed in a net...
CEC
2011
IEEE
12 years 11 months ago
2011
IEEE
—Stochastic relaxation aims at finding the minimum of a fitness function by identifying a proper sequence of distributions, in a given model, that minimize the expected value o...
IJCV
2011
13 years 2 months ago
2011
We examine the underlying structure of popular algorithms for variational methods used in image processing. We focus here on operator splittings and Bregman methods based on a uni...
CDC
2010
IEEE
13 years 6 months ago
2010
IEEE
Abstract-- In robot deployment problems, the fundamental issue is to optimize a steady state performance measure that depends on the spatial configuration of a group of robots. For...
TSP
2008
13 years 11 months ago
2008
This paper considers localization of a source or a sensor from distance measurements. We argue that linear algorithms proposed for this purpose are susceptible to poor noise perfor...
IJON
2007
13 years 11 months ago
2007
This paper addresses the problem of variable ranking for Support Vector Regression. The relevance criteria that we proposed are based on leave-one-out bounds and some variants and...
CORR
2010
Springer
13 years 11 months ago
2010
Springer
Cyclic coordinate descent is a classic optimization method that has witnessed a resurgence of interest in machine learning. Reasons for this include its simplicity, speed and stab...