Abstract
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.
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Castillo, E., Hadi, A.S., Lacruz, B. (2001). Optimal Transformations in Multiple Linear Regression Using Functional Networks. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_36
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DOI: https://doi.org/10.1007/3-540-45720-8_36
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