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KDD
2008
ACM
181views Data Mining» more  KDD 2008»
14 years 7 months ago
Learning subspace kernels for classification
Kernel methods have been applied successfully in many data mining tasks. Subspace kernel learning was recently proposed to discover an effective low-dimensional subspace of a kern...
Jianhui Chen, Shuiwang Ji, Betul Ceran, Qi Li, Min...
KDD
2008
ACM
147views Data Mining» more  KDD 2008»
14 years 7 months ago
Extracting shared subspace for multi-label classification
Multi-label problems arise in various domains such as multitopic document categorization and protein function prediction. One natural way to deal with such problems is to construc...
Shuiwang Ji, Lei Tang, Shipeng Yu, Jieping Ye
CVPR
2007
IEEE
14 years 9 months ago
Variational Bayes Based Approach to Robust Subspace Learning
This paper presents a new algorithm for the problem of robust subspace learning (RSL), i.e., the estimation of linear subspace parameters from a set of data points in the presence...
Takayuki Okatani, Koichiro Deguchi
KDD
2007
ACM
197views Data Mining» more  KDD 2007»
14 years 7 months ago
Learning the kernel matrix in discriminant analysis via quadratically constrained quadratic programming
The kernel function plays a central role in kernel methods. In this paper, we consider the automated learning of the kernel matrix over a convex combination of pre-specified kerne...
Jieping Ye, Shuiwang Ji, Jianhui Chen
GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
13 years 11 months ago
Evolutionary learning with kernels: a generic solution for large margin problems
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
Ingo Mierswa