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» A Boosting Algorithm for Regression
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ICPR
2010
IEEE
15 years 7 months ago
Localized Multiple Kernel Regression
Multiple kernel learning (MKL) uses a weighted combination of kernels where the weight of each kernel is optimized during training. However, MKL assigns the same weight to a kerne...
Mehmet Gönen, Ethem Alpaydin
CSDA
2010
111views more  CSDA 2010»
15 years 4 months ago
Mixtures of regressions with predictor-dependent mixing proportions
We extend the standard mixture of linear regressions model by allowing mixing proportions to be modeled nonparametrically as a function of the predictors. This framework allows fo...
D. S. Young, D. R. Hunter
PR
2006
89views more  PR 2006»
15 years 4 months ago
Gaussian fields for semi-supervised regression and correspondence learning
Gaussian fields (GF) have recently received considerable attention for dimension reduction and semi-supervised classification. In this paper we show how the GF framework can be us...
Jakob J. Verbeek, Nikos A. Vlassis
IJON
2010
159views more  IJON 2010»
14 years 11 months ago
Model predictive flight control using adaptive support vector regression
This paper explores an application of support vector regression (SVR) to model predictive control (MPC). SVR is employed to identify a dynamic system from input-output data, and t...
Jongho Shin, H. Jin Kim, Sewook Park, Youdan Kim
JMLR
2010
112views more  JMLR 2010»
14 years 11 months ago
Sparse Spectrum Gaussian Process Regression
We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsify the spectral representation of the GP. This leads to a simple, practical algo...
Miguel Lázaro-Gredilla, Joaquin Quiñ...