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» Nonlinear functional regression: a functional RKHS approach
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JMLR
2010
186views more  JMLR 2010»
13 years 2 months ago
Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting
We address instance-based learning from a perceptual organization standpoint and present methods for dimensionality estimation, manifold learning and function approximation. Under...
Philippos Mordohai, Gérard G. Medioni
SDM
2007
SIAM
106views Data Mining» more  SDM 2007»
13 years 8 months ago
Approximating Representations for Large Numerical Databases
The paper introduces a notion of support for realvalued functions. It is shown how to approximate supports of a large class of functions based on supports of so called polynomial ...
Szymon Jaroszewicz, Marcin Korzen
BMVC
2010
13 years 5 months ago
Local Gaussian Processes for Pose Recognition from Noisy Inputs
Gaussian processes have been widely used as a method for inferring the pose of articulated bodies directly from image data. While able to model complex non-linear functions, they ...
Martin Fergie, Aphrodite Galata
ISBI
2006
IEEE
14 years 8 months ago
Landmark matching on the sphere using distance functions
Nonlinear registration of 3D surfaces is important in many medical imaging applications, including the mapping of longitudinal changes in anatomy, or of multi-subject functional M...
Natasha Lepore, Alex D. Leow, Paul M. Thompson
ICML
2004
IEEE
14 years 8 months ago
Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model
Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. There are...
Zhihua Zhang, James T. Kwok, Dit-Yan Yeung