GECCO
2007
Springer
13 years 10 months ago
2007
Springer
A limited memory version of the covariance matrix adaptation evolution strategy (CMA-ES) is presented. This algorithm, L-CMA-ES, improves the space and time complexity of the CMA-...
IROS
2007
IEEE
13 years 11 months ago
2007
IEEE
— In this paper, we present an Extended Kalman Filter (EKF)-based estimator for simultaneous localization and mapping (SLAM) with processing requirements that are linear in the n...
CEC
2007
IEEE
13 years 11 months ago
2007
IEEE
— Gaussian models are widely adopted in continuous Estimation of Distribution Algorithms (EDAs). In this paper, we analyze continuous EDAs and show that they don’t always work ...
CVPR
2008
IEEE
13 years 11 months ago
2008
IEEE
Recently, 2DPCA and its variants have attracted much attention in face recognition area. In this paper, some efforts are made to discover the underlying fundaments of these method...
KI
2009
Springer
13 years 11 months ago
2009
Springer
Many practical optimization problems are constrained black boxes. Covariance Matrix Adaptation Evolution Strategies (CMA-ES) belong to the most successful black box optimization me...
EMO
2009
Springer
13 years 11 months ago
2009
Springer
The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) is a variable-metric algorithm for real-valued vector optimization. It maintains a parent population...
ICRA
2009
IEEE
13 years 11 months ago
2009
IEEE
—In this work, we utilize a recently studied more accurate range noise model for 3D sensors to derive from scratch the expressions for the optimum plane which best fits a point-...
ICASSP
2009
IEEE
13 years 11 months ago
2009
IEEE
Reducing the number of secondary data used to estimate the Clutter Covariance Matrix (CCM) for Space Time Adaptive Processing (STAP) techniques is still an active research topic. ...
CVPR
2009
IEEE
13 years 11 months ago
2009
IEEE
Given an image region of pixels, second order statistics can be used to construct a descriptor for object representation. One example is the covariance matrix descriptor, which sh...
ECCV
2002
Springer
14 years 6 months ago
2002
Springer
Abstract. Principal Component Analysis (PCA) is one of the most popular techniques for dimensionality reduction of multivariate data points with application areas covering many bra...