A regularized logistic regression based model for supervised learning
Carlos Brito-Pacheco, Carlos Brito‐Loeza, Anabel Martín-González · Journal of Algorithms & Computational Technology · 2020
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.