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Springer International Publishing
Reference71 articles.
1. A. Agapitos, A. Brabazon, M. O’Neill, Controlling overfitting in symbolic regression based on a bias/variance error decomposition, in Parallel Problem Solving from Nature-PPSN XII (Springer, Berlin, 2012), pp. 438–447
2. S.-i. Amari, S. Wu, Improving support vector machine classifiers by modifying kernel functions. Neural Netw. 12(6), 783–789 (1999)
3. D.A. Augusto, H.J. Barbosa, Symbolic regression via genetic programming, in Proceedings. Vol. 1. Sixth Brazilian Symposium on Neural Networks (IEEE, Piscataway, 2000), pp. 173–178
4. R.M.A. Azad, C. Ryan, Variance based selection to improve test set performance in genetic programming, in Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO) (2011), pp. 1315–1322
5. W. Banzhaf, P. Nordin, R.E. Keller, F.D. Francone, Genetic Programming—An Introduction: On the Automatic Evolution of Computer Programs and Its Applications (dpunkt-Verlag and Morgan Kaufmann, San Francisco, 1998)
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