Feature-dependent label noise learning for big data analytics

该项目旨在使机器能够强大地利用来自大数据的特征相关标签噪声。该项目预计将产生探索和利用弱监督信息的潜力，以更好地理解，解释和推断大数据。该项目的预期成果包括在现实世界场景中使用标签噪声学习的理论基础，以及下一代智能系统，以适应嘈杂的大数据。该项目应通过在人工智能，网络安全和大数据分析领域的应用，使科学，社会和经济在国内和国际上受益。

This project aims to equip machines with the ability to robustly harness feature-dependent label noise from big data. The project expects to produce the potential to explore and exploit the weakly supervised information to better understand, interpret, and infer big data. Expected outcomes of this project include theoretical foundations for learning with label noise in the real-world scenarios and the next generation of intelligent systems to accommodate noisily annotated big data. This project should benefit science, society, and the economy nationally and internationally through the applications in the areas of artificial intelligence, cybersecurity, and big data analytics.