A regularized logistic regression based model for supervised learning. (November 2020)

• Record Type: Journal Article
• Authors: Brito-Pacheco, Carlos; Brito-Loeza, Carlos; Martin-Gonzalez, Anabel
• Is Part Of: Journal of algorithms & computational technology. Volume 14(2020)
• Publication Date: 2020-11
• Subjects: Supervised learning -- variational methods -- regularization; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748302620971535
• Languages: English
• ISSNs: 1748-3018

Abstract

In this work, we introduce a new regularized logistic model for the supervised classification problem. Current logistic models have become the preferred tools for supervised classification in many situations. They mostly use either L 1 or L 2 regularization of the weight vector of parameters. Here we take a different approach by applying regularization not to the weight vector but to the gradient vector of the function representing the separating hyper-surface. We present the mathematical analysis of the model in its continuous setting and provide experimental evidence to show that the new model is competitive with state of the art models.