Article
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A Novel Tsetlin Machine with Enhanced Generalization
Version 1
: Received: 20 September 2024 / Approved: 23 September 2024 / Online: 23 September 2024 (14:18:38 CEST)
How to cite: Anjum, U.; Zhan, J. A Novel Tsetlin Machine with Enhanced Generalization. Preprints 2024, 2024091767. https://doi.org/10.20944/preprints202409.1767.v1 Anjum, U.; Zhan, J. A Novel Tsetlin Machine with Enhanced Generalization. Preprints 2024, 2024091767. https://doi.org/10.20944/preprints202409.1767.v1
Abstract
Tsetlin machine (TM) is a novel machine learning approach that implements propositional logic to perform different tasks like classification and regression. The TM can not only achieves competitive accuracy in these tasks but it is also able to provide results that are explainable and easy to implement using simple hardware. The TM learns using clauses based off the features of the data. Final classification is done using a combination of the clauses. In this paper, we propose the novel idea of adding regularizers to TM, called as Regularized TM (RegTM), to improve generalization. Regularizers have been widely used in machine learning to improve accuracy. We explore different regularization strategies and how they would influence performance. We show the feasibility of our methodology by different experiments on benchmark datasets.
Keywords
Tsetlin Machines; regularization; classification; propositional logic; explainable AI; few-shot learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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