Transformation and weighting in regression
Transformation and weighting in regression
A Minimax Method for Learning Functional Networks
Neural Processing Letters
Software reliability identification using functional networks: A comparative study
Expert Systems with Applications: An International Journal
Hybrid computational models for the characterization of oil and gas reservoirs
Expert Systems with Applications: An International Journal
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Functional networks are used to determine the optimal transformations to be applied to the response and the predictor variables in linear regression. The main steps required to build the functional network: selection of the initial topology, simplification of the initial functional network, uniqueness of representation, and learning the parameters are discussed, and illustrated with some examples.