Learnware: on the future of machine learning

Zhi-Hua Zhou1
1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

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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