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» A Boosting Algorithm for Regression
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AUTOMATICA
2006
112views more  AUTOMATICA 2006»
15 years 2 months ago
Joint identification of plant rational models and noise distribution functions using binary-valued observations
System identification of plants with binary-valued output observations is of importance in understanding modeling capability and limitations for systems with limited sensor inform...
Le Yi Wang, Gang George Yin, Ji-Feng Zhang
105
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CSDA
2006
108views more  CSDA 2006»
15 years 2 months ago
Repeated median and hybrid filters
Standard median filters preserve abrupt shifts (edges) and remove impulsive noise (outliers) from a constant signal but they deteriorate in trend periods. Finite impulse response ...
Roland Fried, Thorsten Bernholt, Ursula Gather
JMLR
2006
156views more  JMLR 2006»
15 years 2 months ago
Large Scale Multiple Kernel Learning
While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic ...
Sören Sonnenburg, Gunnar Rätsch, Christi...
JMLR
2007
104views more  JMLR 2007»
15 years 2 months ago
Learnability of Gaussians with Flexible Variances
Gaussian kernels with flexible variances provide a rich family of Mercer kernels for learning algorithms. We show that the union of the unit balls of reproducing kernel Hilbert s...
Yiming Ying, Ding-Xuan Zhou
112
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NA
2007
120views more  NA 2007»
15 years 2 months ago
On choosing "optimal" shape parameters for RBF approximation
Many radial basis function (RBF) methods contain a free shape parameter that plays an important role for the accuracy of the method. In most papers the authors end up choosing this...
Gregory E. Fasshauer, Jack G. Zhang