[1] He W, Zou F, and Liang Q. Online learning with sparse labels, Concurrency and Computation: Practice and Experience, 2019, 31(23):e4480 (SCI) [2] 贺文武,刘国买,刘建华. 新工科专业育人共同体与学习共同体构建研究-以数据科学与大数据技术为例. 教育评论,2018,8:46-51(核心) [3] He W, Liu Y. To regularize or not: Revisiting SGD with simple algorithms and experimental studies, Expert Systems With Applications (SCI 1区), 2018, 112:1-14. [4]贺文武. 高效核学习方法及其在预测中的应用(专著). 中国铁道出版社. 2017.12出版. [5]贺文武,刘国买. 数据科学与大数据技术专业核心课程建设的探索与研究,教育评论,2017,11:31-35. (核心) [6]He W, Kwok J T, Zhu J, and Liu Y. A note on the unification of adaptive online learning. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(5): 1178-1191. (SCI 1区) [7] He W, Kwok T-Y J. Simple Randomized Algorithms for Online Learning with Kernels, Neural Networks, 2014, 60:17–24. (SCI 1区) [8] He W, Wu S. A Kernel-Based Perceptron with Dynamic Memory. Neural Networks, 2012, 25:106-113. (SCI 1区) [9] Hui J, He W. Grey relational grade in local support vector regression for financial time series prediction.Expert Systems with Applications, 2012, 39: 2256 -2262.(SCI 1区) [10] He W. Limited Stochastic Meta-Descent for Kernel-Based Online Learning. Neural Computation, 2009, 21(9): 2667-2686. (SCI) [11] He W, Wang Z, Jiang H. Model optimizing and feature selecting for support vector regression in time series forecasting. Neurocomputing, 2008, 72(1-3): 600-611. (SCI 1 区) [12] He W, Wang Z.Direct Simplification for Kernel Regression Machines. Neurocomputing ,2008, 71(16-18): 3602–3606. (SCI 1 区) [13] He W. Forecasting electricity load with optimized local learning models. International Journal of Electrical Power & Energy Systems, 2008, 30(10): 603-608. (SCI 1 区) |