探索超维向量对Tsetlin机的影响
Exploring Effects of Hyperdimensional Vectors for Tsetlin Machines
AI总结：
本文提出基于超维向量的方法，解决Tsetlin机处理复杂数据结构布尔化难题，显著提升模型准确率与学习速度，为其开辟新研究方向。
摘要：
Tsetlin机（TMs）在多个应用领域取得了成功，能够高效处理输入数据的布尔表示。然而，将序列、图、图像、信号光谱、化合物和自然语言等复杂数据结构布尔化并非易事。本文提出了一种基于超维向量的方法，用于表达与任意输入数据相关联的任意大的概念集合。利用超维空间构建向量极大地扩展了TMs的容量和灵活性。我们展示了如何根据所提出的方法对图像、化合物和自然语言文本进行编码，以及由此产生的HV驱动的TM如何在知名基准上实现显著更高的准确率和更快的学习速度。我们的结果为TMs开辟了新的研究方向，即如何扩展和利用在超空间中操作的优势，包括新的布尔化策略、TM推理和学习的优化，以及新的TM应用。
英文摘要：
Tsetlin machines (TMs) have been successful in several application domains, operating with high efficiency on Boolean representations of the input data. However, Booleanizing complex data structures such as sequences, graphs, images, signal spectra, chemical compounds, and natural language is not trivial. In this paper, we propose a hypervector (HV) based method for expressing arbitrarily large sets of concepts associated with any input data. Using a hyperdimensional space to build vectors drastically expands the capacity and flexibility of the TM. We demonstrate how images, chemical compounds, and natural language text are encoded according to the proposed method, and how the resulting HV-powered TM can achieve significantly higher accuracy and faster learning on well-known benchmarks. Our results open up a new research direction for TMs, namely how to expand and exploit the benefits of operating in hyperspace, including new booleanization strategies, optimization of TM inference and learning, as well as new TM applications.
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。
