- author = "Cesar L. Alonso and Jose Luis Montana and Cruz Enrique Borges",
- title = "Genetic Programming Model Regularization",
- booktitle = "Computational Intelligence",
- publisher = "Springer",
- year = "2016",
- editor = "Kurosh Madani and Antonio Dourado and Agostinho Rosa and Joaquim Filipe and Janusz Kacprzyk",
- series = "Springer Professional Technik",
- note = "Selected extended papers from the fifth International Joint Conference on Computational Intelligence (IJCCI 2013), held in Vilamoura, Algarve, Portugal, from 20 to 22 September 2013",
- keywords = "genetic algorithms, genetic programming, VC dimension",
- isbn13 = "978-3-319-23391-8",
-
URL = "
https://www.springerprofessional.de/en/genetic-programming-model-regularization/6856568",
- abstract = "We propose a tool for controlling the complexity of Genetic Programming models. The tool is supported by the theory of Vapnik-Chervonekis dimension (VCD) and is combined with a novel representation of models named straight line program. Experimental results, implemented on conventional algebraic structures (such as polynomials), show that the empirical risk, penalized by suitable upper bounds for the Vapnik-Chervonenkis dimension, gives a generalization error smaller than the use of statistical conventional techniques such as Bayesian or Akaike information criteria.",
-
notes = "See also \cite{conf/ijcci/AlonsoMB13}
Using incollection rather than inproceedings as the book seems more edited than a usual conference proceedings, see DBLP http://dblp.uni-trier.de/db/conf/ijcci/ijcci2013.html
https://www.springerprofessional.de/en/computational-intelligence/6652954",
