# Algebraic Machine Learning

@article{MartinMaroto2018AlgebraicML, title={Algebraic Machine Learning}, author={Fernando Martin-Maroto and Gonzalo G. de Polavieja}, journal={ArXiv}, year={2018}, volume={abs/1803.05252} }

Machine learning algorithms use error function minimization to fit a large set of parameters in a preexisting model. However, error minimization eventually leads to a memorization of the training dataset, losing the ability to generalize to other datasets. To achieve generalization something else is needed, for example a regularization method or stopping the training when error in a validation dataset is minimal. Here we propose a different approach to learning and generalization that is… CONTINUE READING

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