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JMLR
2012
7 years 4 months ago
Minimax-Optimal Rates For Sparse Additive Models Over Kernel Classes Via Convex Programming
Sparse additive models are families of d-variate functions with the additive decomposition f∗ = ∑j∈S f∗ j , where S is an unknown subset of cardinality s d. In this paper,...
Garvesh Raskutti, Martin J. Wainwright, Bin Yu
WACV
2012
IEEE
7 years 10 months ago
Kernel analysis over Riemannian manifolds for visual recognition of actions, pedestrians and textures
A convenient way of analysing Riemannian manifolds is to embed them in Euclidean spaces, with the embedding typically obtained by flattening the manifold via tangent spaces. This...
Mehrtash Tafazzoli Harandi, Conrad Sanderson, Arno...
DAGM
2011
Springer
8 years 2 months ago
Using Landmarks as a Deformation Prior for Hybrid Image Registration
Hybrid registration schemes are a powerful alternative to fully automatic registration algorithms. Current methods for hybrid registration either include the landmark information a...
Marcel Lüthi, Christoph Jud, Thomas Vetter
AAAI
2011
8 years 2 months ago
Transfer Learning by Structural Analogy
Transfer learning allows knowledge to be extracted from auxiliary domains and be used to enhance learning in a target domain. For transfer learning to be successful, it is critica...
Hua-Yan Wang, Qiang Yang
ICASSP
2011
IEEE
8 years 6 months ago
Theoretical analyses on a class of nested RKHS's
One of central topics of kernel machines in the field of machine learning is a model selection, especially a selection of a kernel or its parameters. In our previous work, we dis...
Akira Tanaka, Hideyuki Imai, Mineichi Kudo, Masaak...
JMIV
2010
115views more  JMIV 2010»
9 years 26 days ago
Image and Video Colorization Using Vector-Valued Reproducing Kernel Hilbert Spaces
Motivated by the setting of reproducing kernel Hilbert space (RKHS) and its extensions considered in machine learning, we propose an RKHS framework for image and video colorizatio...
Minh Ha Quang, Sung Ha Kang, Triet M. Le
FOCM
2006
97views more  FOCM 2006»
9 years 2 months ago
Learning Rates of Least-Square Regularized Regression
This paper considers the regularized learning algorithm associated with the leastsquare loss and reproducing kernel Hilbert spaces. The target is the error analysis for the regres...
Qiang Wu, Yiming Ying, Ding-Xuan Zhou
FOCM
2008
140views more  FOCM 2008»
9 years 2 months ago
Online Gradient Descent Learning Algorithms
This paper considers the least-square online gradient descent algorithm in a reproducing kernel Hilbert space (RKHS) without explicit regularization. We present a novel capacity i...
Yiming Ying, Massimiliano Pontil
ADCM
2007
114views more  ADCM 2007»
9 years 2 months ago
Convergence analysis of online algorithms
In this paper, we are interested in the analysis of regularized online algorithms associated with reproducing kernel Hilbert spaces. General conditions on the loss function and st...
Yiming Ying
ICASSP
2010
IEEE
9 years 2 months ago
A kernel mean matching approach for environment mismatch compensation in speech recognition
The mismatch between training and test environmental conditions presents a challenge to speech recognition systems. In this paper, we investigate an approach for matching the dist...
Abhishek Kumar, John H. L. Hansen
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