Fontenla-Romero, ÓscarGuijarro-Berdiñas, BerthaPérez-Sánchez, Beatriz2026-01-142026-01-142021-08Fontenla-Romero, O., Guijarro-Berdiñas, B., Pérez-Sánchez, B. (2021). Regularized One-Layer Neural Networks for Distributed and Incremental Environments. In: Rojas, I., Joya, G., Català, A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science(), vol 12862. Springer, Cham. https://doi.org/10.1007/978-3-030-85099-9_28978-3-030-85099-9978-3-030-85098-21611-3349https://hdl.handle.net/2183/46869This version of the conference paper has been accepted for publication, after peer review, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-85099-9_28 . Presented at: 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021.[Abstract]: Deploying machine learning models at scale is still a major challenge; one reason is that performance degrades when they are put into production. It is therefore very important to ensure the maximum possible generalization capacity of the models and regularization plays a key role in avoiding overfitting. We describe Regularized One-Layer Artificial Neural Network (ROLANN), a novel regularized training method for one-layer neural networks. Despite its simplicity, this network model has several advantages: it is noniterative, has low complexity, and is capable of incremental and privacy-preserving distributed learning, while maintaining or improving accuracy over other state-of-the-art methods as demonstrated by the experimental study in which it has been compared with ridge regression, lasso and elastic net over several data sets.eng©2021 Springer Nature Switzerland AG. This version of the conference paper is subject to Springer Nature’s AM terms of use.RegularizationBig dataIncremental learningDistributed learningPrivacy-preservingSingular value decompositionconference outputopen access10.1007/978-3-030-85099-9_28