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» Heteroscedastic Gaussian process regression
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UAI
2008
15 years 1 months ago
Modelling local and global phenomena with sparse Gaussian processes
Much recent work has concerned sparse approximations to speed up the Gaussian process regression from the unfavorable O(n3 ) scaling in computational time to O(nm2 ). Thus far, wo...
Jarno Vanhatalo, Aki Vehtari
BMCBI
2007
194views more  BMCBI 2007»
14 years 11 months ago
Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data
Background: Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more importa...
Xin Zhao, Leo Wang-Kit Cheung
JEI
2010
123views more  JEI 2010»
14 years 6 months ago
Estimating reflectance from multispectral camera responses based on partial least-squares regression
Abstract. In multispectral imaging systems, the accuracy of reflectance estimation can be degraded by the nonlinearity in imaging process, which is due to non-Gaussian distribution...
Hui-Liang Shen, Hui-Jiang Wan, Zhe-Chao Zhang
ICDM
2009
IEEE
163views Data Mining» more  ICDM 2009»
15 years 6 months ago
Kernel Conditional Quantile Estimation via Reduction Revisited
Quantile regression refers to the process of estimating the quantiles of a conditional distribution and has many important applications within econometrics and data mining, among ...
Novi Quadrianto, Kristian Kersting, Mark D. Reid, ...
ML
2002
ACM
140views Machine Learning» more  ML 2002»
14 years 11 months ago
A Probabilistic Framework for SVM Regression and Error Bar Estimation
In this paper, we elaborate on the well-known relationship between Gaussian Processes (GP) and Support Vector Machines (SVM) under some convex assumptions for the loss functions. ...
Junbin Gao, Steve R. Gunn, Chris J. Harris, Martin...