A regularized logistic regression based model for supervised learning

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 L1 or L2 regularization of the weight vector of parameters. Here we...

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Bibliographic Details
Published in:Journal of algorithms & computational technology 2020-11, Vol.14
Main Authors: Brito-Pacheco, Carlos, Brito-Loeza, Carlos, Martin-Gonzalez, Anabel
Format: Article
Language:English
Subjects:
Classification
Regression analysis
Regression models
Regularization
Supervised learning
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Summary: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 L1 or L2 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.
ISSN:1748-3026
1748-3018
1748-3026
DOI:10.1177/1748302620971535