Author:
Song Xin,Qin Shizhen,Niu Shaokai,Wang Yan
Publisher
Springer International Publishing
Reference24 articles.
1. Xu, W., Qin, Z., Chang, Y.: Clustering feature decision trees for semi-supervised classification from high-speed data streams. J. Zhejiang Univ. Sci. C 12(8), 615–628 (2011)
2. Brzezinski, D., Stefanowski, J.: Reacting to different types of concept drift: the accuracy updated ensemble algorithm. IEEE Transact. Neural Netw. Learn. Syst. 25(1), 81–94 (2014)
3. Schlimmer, J.C., Granger, R.: Incremental learning from noisy data. Mach. Learn. 1(3), 317–354 (1986)
4. Gama, J., Zliobaite, I., Bifet, A.: A survey on concept drift adaptation. ACM Comput. Surv. 46(4), 44 (2014)
5. Dasu, T., Krishnan, S., Venkatasubramanian, S.: An information-theoretic approach to detecting changes in multi-dimensional data streams. In: Proceedings of the Symposium on the Interface of Statistics, Computing Science, and Applications, pp. 1–24 (2006)