References
Li N, Tsang IW, Zhou Z H. Efficient optimization of performance measures by classifier adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1370–1382
Pan S J, Yang Q. A survey of transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345–1359
Sugiyama M, Kawanabe M. Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation. Cambridge, MA: MIT Press, 2012
Da Q, Yu Y, Zhou Z H. Learning with augmented class by exploiting unlabeled data.. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence. 2014, 1760–1766
Mu X, Ting K M, Zhou Z H. Classification under streaming emerging new classes: a solution using completely random trees. CORR abs/1605.09131, 2016
Hou C, Zhou Z H. One-pass learning with incremental and decremental features. CORR abs/1605.09082, 2016
Dietterich T G. Towards robust artificial intelligence. AAAI Presidential Address at the 30th AAAI Conference on Artificial Intelligence. 2016
Zhou Z H, Jiang Y, Chen S F. Extracting symbolic rules from trained neural network ensembles. AI Communications, 2003, 16(1): 3–15
Zhou Z H, Jiang Y. NeC4.5: Neural ensemble based C4.5. IEEE Transactions on Knowledge and Data Engineering, 2004, 16(6): 770–773
Zhou Z H. Ensemble Methods: Foundations and Algorithms. Boca Raton, FL: CRC Press, 2012
Author information
Authors and Affiliations
Corresponding author
Additional information
Zhi-Hua Zhou is a professor at the Department of Computer Science and Technology, Nanjing University, China. He is the standing deputy director of the National Key Laboratory for Novel Software Technology, and founding director of LAMDA. He is a fellow of the AAAI, IEEE, IAPR and CCF, and also an ACM Distinguished Scientist. His main research interests are in artificial intelligence, machine learning and data mining.
Rights and permissions
About this article
Cite this article
Zhou, ZH. Learnware: on the future of machine learning. Front. Comput. Sci. 10, 589–590 (2016). https://doi.org/10.1007/s11704-016-6906-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11704-016-6906-3