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Block-GP: Scalable Gaussian Process Regression for Multimodal Data
2010
2010 IEEE International Conference on Data Mining
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 algorithms such as linear regression or neural networks attempt to fit the target variable as a function of the input variables without regard to the underlying joint distribution of the variables. As a result, these global models are not sensitive to variations in the local structure of the input space. Several algorithms,
doi:10.1109/icdm.2010.38
dblp:conf/icdm/DasS10
fatcat:cjgb7j5ltnbcjidi23x2dlxzq4