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Context-Aware Deep Sequence Learning with Multi-View Factor Pooling for Time Series Classification.

, , , , , and . BigData, page 959-966. IEEE, (2018)

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Comparing SVM ensembles for imbalanced datasets., , and . ISDA, page 651-657. IEEE, (2010)Modeling lightcurves for improved classification of astronomical objects., , , , , and . Statistical Analysis and Data Mining, 9 (1): 1-11 (2016)Data challenges of time domain astronomy., , , , , and . Distributed and Parallel Databases, 30 (5-6): 371-384 (2012)View-Adaptive Weighted Deep Transfer Learning for Distributed Time-Series Classification., , , , , and . COMPSAC (1), page 373-381. IEEE, (2019)Feature selection strategies for classifying high dimensional astronomical data sets., , , , , , , , , and 1 other author(s). BigData, page 35-41. IEEE Computer Society, (2013)Some Pattern Recognition Challenges in Data-Intensive Astronomy., , , , , , and . ICPR (1), page 856-863. IEEE Computer Society, (2006)Data Understanding using Semi-Supervised Clustering., , , and . CIDU, page 118-123. IEEE, (2012)From Sky to Earth: Data Science Methodology Transfer., , , , and . Astroinformatics, volume 12 of Proceedings of the International Astronomical Union, page 17-26. Cambridge University Press, (2016)From stars to patients: Lessons from space science and astrophysics for health care informatics., , , and . BigData, page 2957-2959. IEEE Computer Society, (2015)The DAME/VO-Neural Infrastructure: an Integrated Data Mining System Support for the Science Community, , , , , , , , , and 6 other author(s). CoRR, (2011)