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» A DC-programming algorithm for kernel selection
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PRL
2008
161views more  PRL 2008»
13 years 8 months ago
Automatic kernel clustering with a Multi-Elitist Particle Swarm Optimization Algorithm
This article introduces a scheme for clustering complex and linearly non-separable datasets, without any prior knowledge of the number of naturally occurring groups in the data. T...
Swagatam Das, Ajith Abraham, Amit Konar
IJCNN
2007
IEEE
14 years 2 months ago
Probability Density Function Estimation Using Orthogonal Forward Regression
— Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression tec...
Sheng Chen, Xia Hong, Chris J. Harris
ICML
2010
IEEE
13 years 9 months ago
On Sparse Nonparametric Conditional Covariance Selection
We develop a penalized kernel smoothing method for the problem of selecting nonzero elements of the conditional precision matrix, known as conditional covariance selection. This p...
Mladen Kolar, Ankur P. Parikh, Eric P. Xing
ISNN
2005
Springer
14 years 2 months ago
Multiple Parameter Selection for LS-SVM Using Smooth Leave-One-Out Error
In least squares support vector (LS-SVM), the key challenge lies in the selection of free parameters such as kernel parameters and tradeoff parameter. However, when a large number ...
Liefeng Bo, Ling Wang, Licheng Jiao
NIPS
1998
13 years 10 months ago
Dynamically Adapting Kernels in Support Vector Machines
The kernel-parameter is one of the few tunable parameters in Support Vector machines, controlling the complexity of the resulting hypothesis. Its choice amounts to model selection...
Nello Cristianini, Colin Campbell, John Shawe-Tayl...