dc.contributor.author | Lei, Jiao | |
dc.contributor.author | Zhang, Xuan | |
dc.contributor.author | Granmo, Ole-Christoffer | |
dc.contributor.author | Abeyrathna, Kuruge Darshana | |
dc.date.accessioned | 2024-10-01T09:07:40Z | |
dc.date.available | 2024-10-01T09:07:40Z | |
dc.date.created | 2022-11-21T16:15:45Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Lei, J., Zhang, X., Granmo, O.- C. & Abeyrathna, K. D. (2022). On the Convergence of Tsetlin Machines for the XOR Operator. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45 (5), 6072-6085. | en_US |
dc.identifier.issn | 0162-8828 | |
dc.identifier.uri | https://hdl.handle.net/11250/3155327 | |
dc.description | Author's Accepted Manuscript. | en_US |
dc.description | Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | |
dc.description.abstract | The Tsetlin Machine (TM) is a novel machine learning algorithm with several distinct properties, including transparent inference and learning using hardware-near building blocks. Although numerous papers explore the TM empirically, many of its properties have not yet been analyzed mathematically. In this article, we analyze the convergence of the TM when input is non-linearly related to output by the XOR-operator. Our analysis reveals that the TM, with just two conjunctive clauses, can converge almost surely to reproducing XOR, learning from training data over an infinite time horizon. Furthermore, the analysis shows how the hyper-parameter T guides clause construction so that the clauses capture the distinct sub-patterns in the data. Our analysis of convergence for XOR thus lays the foundation for analyzing other more complex logical expressions. These analyses altogether, from a mathematical perspective, provide new insights on why TMs have obtained the state-of-the-art performance on several pattern recognition problems. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | IEEE | en_US |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no | * |
dc.title | On the Convergence of Tsetlin Machines for the XOR Operator | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | acceptedVersion | en_US |
dc.rights.holder | © 2022 IEEE | en_US |
dc.subject.nsi | VDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550 | en_US |
dc.source.pagenumber | 6072-6085 | en_US |
dc.source.volume | 45 | en_US |
dc.source.journal | IEEE Transactions on Pattern Analysis and Machine Intelligence | en_US |
dc.source.issue | 5 | en_US |
dc.identifier.doi | https://doi.org/10.1109/TPAMI.2022.3203150 | |
dc.identifier.cristin | 2077611 | |
dc.relation.project | Universitetet i Agder: CAIR | en_US |
cristin.ispublished | true | |
cristin.qualitycode | 1 | |