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PR
2007
139views more  PR 2007»
15 years 1 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
143
Voted
ICDM
2010
IEEE
187views Data Mining» more  ICDM 2010»
15 years 7 days ago
Financial Forecasting with Gompertz Multiple Kernel Learning
Financial forecasting is the basis for budgeting activities and estimating future financing needs. Applying machine learning and data mining models to financial forecasting is both...
Han Qin, Dejing Dou, Yue Fang
JMLR
2010
206views more  JMLR 2010»
14 years 9 months ago
Learning Translation Invariant Kernels for Classification
Appropriate selection of the kernel function, which implicitly defines the feature space of an algorithm, has a crucial role in the success of kernel methods. In this paper, we co...
Sayed Kamaledin Ghiasi Shirazi, Reza Safabakhsh, M...
ICML
2010
IEEE
15 years 3 months ago
Fast Neighborhood Subgraph Pairwise Distance Kernel
We introduce a novel graph kernel called the Neighborhood Subgraph Pairwise Distance Kernel. The kernel decomposes a graph into all pairs of neighborhood subgraphs of small radius...
Fabrizio Costa, Kurt De Grave
67
Voted
ICML
2003
IEEE
16 years 3 months ago
Learning with Idealized Kernels
James T. Kwok, Ivor W. Tsang