LUNAR：用于漂移数据流的元胞自动机
LUNAR: Cellular Automata for Drifting Data Streams
摘要：
随着以快速流形式产生的大量数据的出现，实时机器学习已经成为现实世界应用中出现的相关性挑战。处理这样的快速流通常需要高存储器和处理资源。此外，它们可能会受到非平稳现象（概念漂移）的影响，学习方法必须检测流数据分布的变化，并适应这些不断变化的条件。在计算资源受到严重限制的实时场景中，缺乏有效和可扩展的解决方案尤其值得注意，因为它发生在小型，众多，互连处理单元的网络中（例如所谓的智能灰尘，实用雾或群机器人范例）。在这项工作中，我们提出了LUNAR，一个简化版本的元胞自动机设计，以成功地满足上述要求。它能够作为一个真正的增量学习者，同时适应漂移的条件。与长期建立和成功的在线学习方法相比，使用合成和真实数据进行的广泛模拟将提供其在分类性能方面的竞争行为的证据。
英文摘要：
With the advent of huges volumes of data produced in the form of fast streams, real-time machine learning has become a challenge of relevance emerging in a plethora of real-world applications. Processing such fast streams often demands high memory and processing resources. In addition, they can be affected by non-stationary phenomena (concept drift), by which learning methods have to detect changes in the distribution of streaming data, and adapt to these evolving conditions. A lack of efficient and scalable solutions is particularly noted in real-time scenarios where computing resources are severely constrained, as it occurs in networks of small, numerous, interconnected processing units (such as the so-called Smart Dust, Utility Fog, or Swarm Robotics paradigms). In this work we propose LUNAR, a streamified version of cellular automata devised to successfully meet the aforementioned requirements. It is able to act as a real incremental learner while adapting to drifting conditions. Extensive simulations with synthetic and real data will provide evidence of its competitive behavior in terms of classification performance when compared to long-established and successful online learning methods.
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。
