On the Equivalence of the Weighted Tsetlin Machine and the Perceptron
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Abstract (arXiv)
Tsetlin Machine (TM) has been gaining popularity as an inherently interpretable machine leaning method that is able to achieve promising performance with low computational complexity on a variety of applications. The interpretability and the low computational complexity of the TM are inherited from the Boolean expressions for representing various sub-patterns. Although possessing favorable properties, TM has not been the go-to method for AI applications, mainly due to its conceptual and theoretical differences compared with perceptrons and neural networks, which are more widely known and well understood. In this paper, we provide detailed insights for the operational concept of the TM, and try to bridge the gap in the theoretical understanding between the perceptron and the TM. More specifically, we study the operational concept of the TM following the analytical structure of perceptrons, showing the resemblance between the perceptrons and the TM. Through the analysis, we indicated that the TM's weight update can be considered as a special case of the gradient weight update. We also perform an empirical analysis of TM by showing the flexibility in determining the clause length, visualization of decision boundaries and obtaining interpretable boolean expressions from TM. In addition, we also discuss the advantages of TM in terms of its structure and its ability to solve more complex problems.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 34 references that are in the map. First posted on arXiv 27 Dec 2022.
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14 of 34 references.
References · 14
- 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
- 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
- 2017Network Dissection: Quantifying Interpretability of Deep Visual Representationscited by 703
- 2017Interpretation of Neural Networks is Fragilecited by 443
- 2017Interpretable Convolutional Neural Networkscited by 161
- 2018The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logiccited by 71
- 2019The Convolutional Tsetlin Machinecited by 37
- 2018Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization with Medical Applicationscited by 24
- 2020On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operatorscited by 23
- 2020Extending the Tsetlin Machine With Integer-Weighted Clauses for Increased Interpretabilitycited by 16
- 2021On the Convergence of Tsetlin Machines for the XOR Operatorcited by 16
- 2021Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin Machinecited by 7
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