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» Model Selection for Kernel Probit Regression
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ESANN
2007
13 years 5 months ago
Model Selection for Kernel Probit Regression
Abstract. The convex optimisation problem involved in fitting a kernel probit regression (KPR) model can be solved efficiently via an iteratively re-weighted least-squares (IRWLS)...
Gavin C. Cawley
IJCNN
2008
IEEE
13 years 10 months ago
Sparse kernel density estimator using orthogonal regression based on D-Optimality experimental design
— A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experi...
Sheng Chen, Xia Hong, Chris J. Harris
ICPR
2004
IEEE
14 years 4 months ago
Efficient Model Selection for Kernel Logistic Regression
Kernel logistic regression models, like their linear counterparts, can be trained using the efficient iteratively reweighted least-squares (IRWLS) algorithm. This approach suggest...
Gavin C. Cawley, Nicola L. C. Talbot
IJCNN
2007
IEEE
13 years 10 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
ICIC
2007
Springer
13 years 9 months ago
A Sparse Kernel Density Estimation Algorithm Using Forward Constrained Regression
Abstract. Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward constrained regression manner. The...
Xia Hong, Sheng Chen, Chris Harris