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NIPS
2003
13 years 11 months ago
Nonstationary Covariance Functions for Gaussian Process Regression
We introduce a class of nonstationary covariance functions for Gaussian process (GP) regression. Nonstationary covariance functions allow the model to adapt to functions whose smo...
Christopher J. Paciorek, Mark J. Schervish
GECCO
2008
Springer
133views Optimization» more  GECCO 2008»
13 years 11 months ago
Using feature-based fitness evaluation in symbolic regression with added noise
Symbolic regression is a popular genetic programming (GP) application. Typically, the fitness function for this task is based on a sum-of-errors, involving the values of the depe...
Janine H. Imada, Brian J. Ross
CVPR
2004
IEEE
14 years 12 months ago
3D Human Pose from Silhouettes by Relevance Vector Regression
We describe a learning based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor pr...
Ankur Agarwal, Bill Triggs
CVPR
2008
IEEE
14 years 12 months ago
On errors-in-variables regression with arbitrary covariance and its application to optical flow estimation
Linear inverse problems in computer vision, including motion estimation, shape fitting and image reconstruction, give rise to parameter estimation problems with highly correlated ...
Björn Andres, Claudia Kondermann, Daniel Kond...
DAC
2007
ACM
14 years 11 months ago
Beyond Low-Order Statistical Response Surfaces: Latent Variable Regression for Efficient, Highly Nonlinear Fitting
The number and magnitude of process variation sources are increasing as we scale further into the nano regime. Today's most successful response surface methods limit us to lo...
Amith Singhee, Rob A. Rutenbar