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JMLR
2010
132views more  JMLR 2010»
9 years 5 months ago
On the Impact of Kernel Approximation on Learning Accuracy
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...
Corinna Cortes, Mehryar Mohri, Ameet Talwalkar
PR
2007
139views more  PR 2007»
9 years 10 months ago
Learning the kernel matrix by maximizing a KFD-based class separability criterion
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...
Dit-Yan Yeung, Hong Chang, Guang Dai
ML
2006
ACM
121views Machine Learning» more  ML 2006»
9 years 10 months ago
Model-based transductive learning of the kernel matrix
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...
Zhihua Zhang, James T. Kwok, Dit-Yan Yeung
JMLR
2006
132views more  JMLR 2006»
9 years 10 months ago
Accurate Error Bounds for the Eigenvalues of the Kernel Matrix
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...
Mikio L. Braun
CORR
2008
Springer
100views Education» more  CORR 2008»
9 years 10 months ago
Learning Isometric Separation Maps
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...
Nikolaos Vasiloglou, Alexander G. Gray, David V. A...
CORR
2008
Springer
114views Education» more  CORR 2008»
9 years 10 months ago
Support Vector Machine Classification with Indefinite Kernels
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...
Ronny Luss, Alexandre d'Aspremont
ICML
2010
IEEE
9 years 11 months ago
Robust Formulations for Handling Uncertainty in Kernel Matrices
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 ...
Sahely Bhadra, Sourangshu Bhattacharya, Chiranjib ...
RIVF
2008
9 years 12 months ago
Simple but effective methods for combining kernels in computational biology
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 ...
Hiroaki Tanabe, Tu Bao Ho, Canh Hao Nguyen, Saori ...
ECML
2004
Springer
10 years 2 months ago
Efficient Hyperkernel Learning Using Second-Order Cone Programming
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...
Ivor W. Tsang, James T. Kwok
ICML
2004
IEEE
10 years 3 months ago
Learning a kernel matrix for nonlinear dimensionality reduction
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 ...
Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul
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