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Feature Extraction for One-Class Classification. In ICANN\/ICONIP. 342--349. David M. J. Tax and Klaus-Robert M\u00fc ller. 2003. Feature Extraction for One-Class Classification. In ICANN\/ICONIP. 342--349."},{"key":"e_1_3_2_2_62_1","volume-title":"Seshia","author":"Tiwari Ashish","year":"2014","unstructured":"Ashish Tiwari , Bruno Dutertre , Dejan Jovanovic , Thomas de Candia , Patrick Lincoln , John M. Rushby , Dorsa Sadigh , and Sanjit A . Seshia . 2014 . Safety envelope for security. In HiCoNS. 85--94. Ashish Tiwari, Bruno Dutertre, Dejan Jovanovic, Thomas de Candia, Patrick Lincoln, John M. Rushby, Dorsa Sadigh, and Sanjit A. Seshia. 2014. Safety envelope for security. In HiCoNS. 85--94."},{"key":"e_1_3_2_2_63_1","first-page":"58","article-title":"The problem of concept drift: definitions and related work","volume":"106","author":"Tsymbal Alexey","year":"2004","unstructured":"Alexey Tsymbal . 2004 . The problem of concept drift: definitions and related work . 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Handling Local Concept Drift with Dynamic Integration of Classifiers: Domain of Antibiotic Resistance in Nosocomial Infections. In IEEE International Symposium on Computer-Based Medical Systems (CBMS). 679--684."},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-68560-1_16"},{"key":"e_1_3_2_2_66_1","volume-title":"Which principal components are most sensitive to distributional changes? arXiv preprint arXiv:1905.06318","author":"Tveten Martin","year":"2019","unstructured":"Martin Tveten . 2019. Which principal components are most sensitive to distributional changes? arXiv preprint arXiv:1905.06318 ( 2019 ). Martin Tveten. 2019. Which principal components are most sensitive to distributional changes? arXiv preprint arXiv:1905.06318 (2019)."},{"key":"e_1_3_2_2_67_1","unstructured":"Vladimir Vapnik Steven E Golowich and Alex J Smola. 1997. Support vector method for function approximation regression estimation and signal processing. In NeurIPS. 281--287. 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