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Regression

  • Thomas A. Runkler
Chapter

Abstract

Regression estimates functional dependencies between features to understand dependencies and to systematically control systems. Linear regression models can be efficiently computed from covariances but are restricted to linear dependencies. Substitution allows us to identify specific nonlinear dependencies by linear regression. Robust regression finds models that are robust against outliers. A popular family of nonlinear regression methods are universal approximators.We present two well-known examples for universal approximators from the field of artificial neural networks: the multilayer perceptron and radial basis function networks. Universal approximators can realize arbitrarily small training errors, but cross-validation is required to find models with low validation errors that generalize well on other data sets. Feature selection allows us to include only relevant features in regression models leading to more accurate models.

Keywords

Feature Selection Hide Layer Radial Basis Function Radial Basis Function Network Multilayer Perceptron 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden 2012

Authors and Affiliations

  1. 1.MünchenGermany

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