加权Tsetlin机与感知机的等价性
On the Equivalence of the Weighted Tsetlin Machine and the Perceptron
摘要：
Tsetlin Machine（TM）作为一种内在可解释的机器学习方法已经越来越受欢迎，该方法能够在各种应用上以低计算复杂性实现有希望的性能。TM的可解释性和低计算复杂性继承自用于表示各种子模式的布尔表达式。虽然具有良好的属性，TM还没有去的方法，人工智能的应用，主要是由于其概念和理论的差异相比，感知器和神经网络，这是更广泛的了解和理解。在本文中，我们提供了详细的见解TM的操作概念，并试图弥合感知器和TM之间的理论理解的差距。更具体地说，我们研究的操作概念的TM以下的感知器的分析结构，显示感知器和TM之间的相似之处。通过分析，我们指出，TM的权重更新可以被认为是梯度权重更新的一种特殊情况。我们还进行了TM的实证分析，显示在确定条款长度，可视化的决策边界和获得可解释的布尔表达式TM的灵活性。此外，我们还讨论了TM在其结构和解决更复杂问题的能力方面的优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
