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SAND: Semi-Supervised Adaptive Novel Class Detection and Classification over Data Stream.

, , and . AAAI, page 1652-1658. AAAI Press, (2016)

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Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2., , , , , , , , , and 10 other author(s). CoLLAs, volume 199 of Proceedings of Machine Learning Research, page 1120-1145. PMLR, (2022)Semi Supervised Adaptive Framework for Classifying Evolving Data Stream., , and . PAKDD (2), volume 9078 of Lecture Notes in Computer Science, page 383-394. Springer, (2015)CPDGA: Change point driven growing auto-encoder for lifelong anomaly detection., , and . Knowl. Based Syst., (2022)On the selection of software defect estimation techniques., , , and . Softw. Test. Verification Reliab., 21 (2): 125-152 (2011)LIFEWATCH: Lifelong Wasserstein Change Point Detection., , , , and . IJCNN, page 1-8. IEEE, (2022)Efficient handling of concept drift and concept evolution over Stream Data., , , , and . ICDE, page 481-492. IEEE Computer Society, (2016)Automatic Identification of Change Points for the System Testing Process., and . COMPSAC (1), page 377-384. IEEE Computer Society, (2006)SAND: Semi-Supervised Adaptive Novel Class Detection and Classification over Data Stream., , and . AAAI, page 1652-1658. AAAI Press, (2016)System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games., , , , , , , , , and 9 other author(s). AIMLSystems, page 12:1-12:9. ACM, (2022)Asymptotic behavior of confidence regions in the change-point problem, and . Journal of Statistical Planning and Inference, 58 (2): 263--282 (Mar 15, 1997)