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ICDM
2010
IEEE
264views Data Mining» more  ICDM 2010»
13 years 10 months ago
Block-GP: Scalable Gaussian Process Regression for Multimodal Data
Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. In many cases, regression algori...
Kamalika Das, Ashok N. Srivastava
INFORMATICALT
2010
49views more  INFORMATICALT 2010»
13 years 11 months ago
Interval Arithmetic Based Optimization in Nonlinear Regression
The optimization problems occurring in nonlinear regression normally cannot be proven unimodal. In the present paper applicability of global optimization algorithms to this problem...
Antanas Zilinskas, Julius Zilinskas
NN
2000
Springer
165views Neural Networks» more  NN 2000»
14 years 10 days ago
Construction of confidence intervals for neural networks based on least squares estimation
We present the theoretical results about the construction of confidence intervals for a nonlinear regression based on least squares estimation and using the linear Taylor expansio...
Isabelle Rivals, Léon Personnaz
BMCBI
2006
187views more  BMCBI 2006»
14 years 18 days ago
Detecting outliers when fitting data with nonlinear regression - a new method based on robust nonlinear regression and the false
Background: Nonlinear regression, like linear regression, assumes that the scatter of data around the ideal curve follows a Gaussian or normal distribution. This assumption leads ...
Harvey J. Motulsky, Ronald E. Brown
ESANN
2004
14 years 1 months ago
On fields of nonlinear regression models
Abstract. In the context of nonlinear regression, we consider the problem of explaining a variable y from a vector x of explanatory variables and from a vector t of conditionning v...
Bruno Pelletier, Robert Frouin
IJCNN
2006
IEEE
14 years 6 months ago
Nonlinear principal component analysis of noisy data
With very noisy data, having plentiful samples eliminates overfitting in nonlinear regression, but not in nonlinear principal component analysis (NLPCA). To overcome this problem...
William W. Hsieh