JMLR
2010
12 years 11 months ago
2010
Kernel approximation is commonly used to scale kernel-based algorithms to applications containing as many as several million instances. This paper analyzes the effect of such appr...
PR
2007
13 years 4 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...
ML
2006
ACM
13 years 4 months ago
2006
ACM
This paper addresses the problem of transductive learning of the kernel matrix from a probabilistic perspective. We define the kernel matrix as a Wishart process prior and construc...
JMLR
2006
13 years 4 months ago
2006
The eigenvalues of the kernel matrix play an important role in a number of kernel methods, in particular, in kernel principal component analysis. It is well known that the eigenva...
CORR
2008
Springer
13 years 4 months ago
2008
Springer
Maximum Variance Unfolding (MVU) and its variants have been very successful in embedding data-manifolds in lower dimensionality spaces, often revealing the true intrinsic dimensio...
CORR
2008
Springer
13 years 4 months ago
2008
Springer
In this paper, we propose a method for support vector machine classification using indefinite kernels. Instead of directly minimizing or stabilizing a nonconvex loss function, our...
ICML
2010
IEEE
13 years 5 months ago
2010
IEEE
We study the problem of uncertainty in the entries of the Kernel matrix, arising in SVM formulation. Using Chance Constraint Programming and a novel large deviation inequality we ...
RIVF
2008
13 years 6 months ago
2008
Complex biological data generated from various experiments are stored in diverse data types in multiple datasets. By appropriately representing each biological dataset as a kernel ...
ECML
2004
Springer
13 years 8 months ago
2004
Springer
The kernel function plays a central role in kernel methods. Most existing methods can only adapt the kernel parameters or the kernel matrix based on empirical data. Recently, Ong e...
ICML
2004
IEEE
13 years 10 months ago
2004
IEEE
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...