IEEE Trans Cybern. 2022 Sep;52(9):8922-8950. doi: 10.1109/TCYB.2021.3061094. Epub 2022 Aug 18.
In recent years, the appearance of the broad learning system (BLS) is poised to revolutionize conventional artificial intelligence methods. It represents a step toward building more efficient and effective machine-learning methods that can be extended to a broader range of necessary research fields. In this survey, we provide a comprehensive overview of the BLS in data mining and neural networks for the first time, focusing on summarizing various BLS methods from the aspects of its algorithms, theories, applications, and future open research questions. First, we introduce the basic pattern of BLS manifestation, the universal approximation capability, and essence from the theoretical perspective. Furthermore, we focus on BLS's various improvements based on the current state of the theoretical research, which further improves its flexibility, stability, and accuracy under general or specific conditions, including classification, regression, semisupervised, and unsupervised tasks. Due to its remarkable efficiency, impressive generalization performance, and easy extendibility, BLS has been applied in different domains. Next, we illustrate BLS's practical advances, such as computer vision, biomedical engineering, control, and natural language processing. Finally, the future open research problems and promising directions for BLSs are pointed out.
近年来，广谱学习系统 (BLS) 的出现有望彻底改变传统的人工智能方法。它代表了朝着构建更高效、更有效的机器学习方法迈出的一步，可以将其扩展到更广泛的必要研究领域。在本次调查中，我们首次在数据挖掘和神经网络中全面概述了 BLS，重点从算法、理论、应用和未来开放研究问题等方面总结了各种 BLS 方法。首先，我们从理论角度介绍了 BLS 表现的基本模式、通用逼近能力和本质。此外，我们专注于基于当前理论研究现状的 BLS 的各种改进，进一步提高了其在一般或特定条件下的灵活性、稳定性和准确性，包括分类、回归、半监督和无监督任务。由于其显著的效率、出色的泛化性能和易于扩展性，BLS 已应用于不同领域。接下来，我们展示了 BLS 在计算机视觉、生物医学工程、控制和自然语言处理等领域的实际进展。最后，指出了 BLS 的未来开放研究问题和有前途的方向。
