  We present an approach for regression problems that employs analytic
continued fractions as a novel representation. Comparative computational
results using a memetic algorithm are reported in this work. Our experiments
included fifteen other different machine learning approaches including five
genetic programming methods for symbolic regression and ten machine learning
methods. The comparison on training and test generalization was performed using
94 datasets of the Penn State Machine Learning Benchmark. The statistical tests
showed that the generalization results using analytic continued fractions
provides a powerful and interesting new alternative in the quest for compact
and interpretable mathematical models for artificial intelligence.
