CVPR
2012
IEEE
12 years 2 months ago
2012
IEEE
The ability to normalize pose based on super-category landmarks can significantly improve models of individual categories when training data are limited. Previous methods have co...
JMLR
2012
12 years 2 months ago
2012
Metric and kernel learning arise in several machine learning applications. However, most existing metric learning algorithms are limited to learning metrics over low-dimensional d...
JMLR
2012
12 years 2 months ago
2012
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,...
EMNLP
2011
13 years 5 days ago
2011
This paper describes a novel approach to the semantic relation detection problem. Instead of relying only on the training instances for a new relation, we leverage the knowledge l...
ICASSP
2011
IEEE
13 years 4 months ago
2011
IEEE
In this paper, we propose a Gaussian Process Regression (GPR) framework for concealment of corrupted motion vectors in predictive video coding of packet video systems. The problem...
ICASSP
2011
IEEE
13 years 4 months ago
2011
IEEE
We applied a multiple kernel learning (MKL) method based on information-theoretic optimization to speaker recognition. Most of the kernel methods applied to speaker recognition sy...
PAMI
2011
13 years 7 months ago
2011
— Kernel mapping is one of the most used approaches to intrinsically derive nonlinear classifiers. The idea is to use a kernel function which maps the original nonlinearly separ...
ICIP
2010
IEEE
13 years 10 months ago
2010
IEEE
A novel multiregion graph cut image partitioning method combined with kernel mapping is presented. A kernel function transforms implicitly the image data into data of a higher dim...
ML
2008
ACM
13 years 11 months ago
2008
ACM
Kernel functions have become an extremely popular tool in machine learning, with an attractive theory as well. This theory views a kernel as implicitly mapping data points into a ...
PR
2007
13 years 12 months ago
2007
The advantage of a kernel method often depends critically on a proper choice of the kernel function. A promising approach is to learn the kernel from data automatically. In this p...