Hybrid reinforcement learning with expert state sequences
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Researchers are developing domain-driven data mining techniques that target actionable knowledge discovery (KDD) in complex domain problems. The domain-driven technique aims to utililize and mine many aspects of intelligence, such as in-depth data, domain expertise, real-time human involvement, process, environment, and social intelligence. It also metasynthesizes its intelligence sources for actionable knowledge discovery. The method works to expose next-generation methodologies for actionable knowledge discovery, identifying ways in which KDD can better contribute to critical domain problems in theory and practice. It undercovers domain-driven techniques to help KDD, strengthen business intelligence in complex enterprise applications. It also reveals applications that effectively deploy domain-driven data mining method,to solve complex practical problems.
Xiaoxiao Guo, Shiyu Chang, et al.
AAAI 2019
Amy Lin, Sujit Roy, et al.
AGU 2024
R. Sebastian, M. Weise, et al.
ECPPM 2022
Conrad Albrecht, Jannik Schneider, et al.
CVPR 2025