Jingming Xue

National University of Defense Technology

Papers

Total Citations

H-Index

About

Jingming Xue is a researcher at the intersection of machine learning and financial technology, with a primary focus on developing efficient, scalable algorithms for real-world decision-making systems. His most cited work, "Incremental multiple kernel extreme learning machine and its application in Robo-advisors" (2018, 22 citations), introduces a novel approach to extreme learning machines by incorporating multiple kernels and an incremental learning framework. This contribution addresses the challenge of adapting machine learning models to dynamic financial environments, enabling robo-advisors to process streaming data and update their predictive capabilities without full retraining. Xue’s work is notable for bridging theoretical advances in kernel methods with practical deployment in automated financial advisory systems, a rapidly growing field. While his citation count reflects a focused, emerging impact, the paper’s application-driven nature highlights his ability to translate complex algorithmic innovations into tangible tools for industry. Xue’s research is particularly relevant for students and researchers exploring online learning, kernel-based models, and the integration of AI into fintech, offering a clear example of how incremental learning can enhance both model efficiency and real-time decision-making in high-stakes domains.