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Convolutional Regression Tsetlin Machine: An Interpretable Approach to Convolutional Regression

Published: 06 September 2021 Publication History

Abstract

The Convolutional Tsetlin Machine (CTM), a variant of Tsetlin Machine (TM), represents patterns as straightforward AND-rules, to address the high computational complexity and the lack of interpretability of Convolutional Neural Networks (CNNs). CTM has shown competitive performance on MNIST, Fashion-MNIST, and Kuzushiji-MNIST pattern classification benchmarks, both in terms of accuracy and memory footprint. In this paper, we propose the Convolutional Regression Tsetlin Machine (C-RTM) that extends the CTM to support continuous output problems in image analysis. C-RTM identifies patterns in images using the convolution operation as in the CTM and then maps the identified patterns into a real-valued output as in the Regression Tsetlin Machine (RTM). The C-RTM thus unifies the two approaches. We evaluated the performance of C-RTM using 72 different artificial datasets, with and without noise in the training data. Our empirical results show the competitive performance of C-RTM compared to two standard CNNs. Additionally, the interpretability of the identified sub-patterns by C-RTM clauses is analyzed and discussed.

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  • (2025)Asymmetric variable depth learning automaton and its application in defending against selfish mining attacks on bitcoinApplied Soft Computing10.1016/j.asoc.2024.112416170(112416)Online publication date: Feb-2025
  • (2024)MATADOR: Automated System-on-Chip Tsetlin Machine Design Generation for Edge Applications2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE58400.2024.10546779(1-6)Online publication date: 25-Mar-2024
  • (2024)Super-Tsetlin: Superconducting Tsetlin MachinesIEEE Transactions on Applied Superconductivity10.1109/TASC.2024.337527534:3(1-12)Online publication date: May-2024

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cover image ACM Other conferences
ICMLT '21: Proceedings of the 2021 6th International Conference on Machine Learning Technologies
April 2021
183 pages
ISBN:9781450389402
DOI:10.1145/3468891
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 06 September 2021

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  1. Convolutional Regression Tsetlin Machine
  2. Convolutional Tsetlin Machine
  3. Regression Tsetlin Machine
  4. Tsetlin Machine

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Cited By

View all
  • (2025)Asymmetric variable depth learning automaton and its application in defending against selfish mining attacks on bitcoinApplied Soft Computing10.1016/j.asoc.2024.112416170(112416)Online publication date: Feb-2025
  • (2024)MATADOR: Automated System-on-Chip Tsetlin Machine Design Generation for Edge Applications2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE58400.2024.10546779(1-6)Online publication date: 25-Mar-2024
  • (2024)Super-Tsetlin: Superconducting Tsetlin MachinesIEEE Transactions on Applied Superconductivity10.1109/TASC.2024.337527534:3(1-12)Online publication date: May-2024

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