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» More generality in efficient multiple kernel learning
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KDD
2008
ACM
181views Data Mining» more  KDD 2008»
14 years 6 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...
ECML
2004
Springer
13 years 10 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
14 years 7 months ago
Distribution kernels based on moments of counts
Many applications in text and speech processing require the analysis of distributions of variable-length sequences. We recently introduced a general kernel framework, rational ker...
Corinna Cortes, Mehryar Mohri
KDD
2006
ACM
180views Data Mining» more  KDD 2006»
14 years 6 months ago
Learning the unified kernel machines for classification
Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...
Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang
DAGM
2010
Springer
13 years 7 months ago
Random Fourier Approximations for Skewed Multiplicative Histogram Kernels
Abstract. Approximations based on random Fourier features have recently emerged as an efficient and elegant methodology for designing large-scale kernel machines [4]. By expressing...
Fuxin Li, Catalin Ionescu, Cristian Sminchisescu