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 L 1 or L 2 regularization of the weight vector of parameters. Here w...

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Main Authors:	Carlos Brito-Pacheco, Carlos Brito-Loeza, Anabel Martin-Gonzalez
Format:	Article
Language:	English
Published:	SAGE Publishing 2020-11-01
Series:	Journal of Algorithms & Computational Technology
Online Access:	https://doi.org/10.1177/1748302620971535

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spelling	doaj-a1c93f818de1410ca2f1042ad2cf27992020-11-25T04:08:42ZengSAGE PublishingJournal of Algorithms & Computational Technology1748-30262020-11-011410.1177/1748302620971535A regularized logistic regression based model for supervised learningCarlos Brito-PachecoCarlos Brito-LoezaAnabel Martin-GonzalezIn 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.https://doi.org/10.1177/1748302620971535
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Carlos Brito-Pacheco Carlos Brito-Loeza Anabel Martin-Gonzalez
spellingShingle	Carlos Brito-Pacheco Carlos Brito-Loeza Anabel Martin-Gonzalez A regularized logistic regression based model for supervised learning Journal of Algorithms & Computational Technology
author_facet	Carlos Brito-Pacheco Carlos Brito-Loeza Anabel Martin-Gonzalez
author_sort	Carlos Brito-Pacheco
title	A regularized logistic regression based model for supervised learning
title_short	A regularized logistic regression based model for supervised learning
title_full	A regularized logistic regression based model for supervised learning
title_fullStr	A regularized logistic regression based model for supervised learning
title_full_unstemmed	A regularized logistic regression based model for supervised learning
title_sort	regularized logistic regression based model for supervised learning
publisher	SAGE Publishing
series	Journal of Algorithms & Computational Technology
issn	1748-3026
publishDate	2020-11-01
description	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.
url	https://doi.org/10.1177/1748302620971535
work_keys_str_mv	AT carlosbritopacheco aregularizedlogisticregressionbasedmodelforsupervisedlearning AT carlosbritoloeza aregularizedlogisticregressionbasedmodelforsupervisedlearning AT anabelmartingonzalez aregularizedlogisticregressionbasedmodelforsupervisedlearning AT carlosbritopacheco regularizedlogisticregressionbasedmodelforsupervisedlearning AT carlosbritoloeza regularizedlogisticregressionbasedmodelforsupervisedlearning AT anabelmartingonzalez regularizedlogisticregressionbasedmodelforsupervisedlearning
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A regularized logistic regression based model for supervised learning

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