{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:32:41Z","timestamp":1740123161518,"version":"3.37.3"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61906165"],"award-info":[{"award-number":["61906165"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["19KJB520064"],"award-info":[{"award-number":["19KJB520064"]}]},{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["61972335"],"award-info":[{"award-number":["61972335"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872313"],"award-info":[{"award-number":["61872313"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s10994-022-06133-8","type":"journal-article","created":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T22:02:31Z","timestamp":1647036151000},"page":"2323-2348","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Online active classification via margin-based and feature-based label queries"],"prefix":"10.1007","volume":"111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4660-2125","authenticated-orcid":false,"given":"Tingting","family":"Zhai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fr\u00e9d\u00e9ric","family":"Koriche","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junwu","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,3,11]]},"reference":[{"unstructured":"Awasthi, P., Balcan, M., Haghtalab, N., & Urner, R. (2015). Efficient learning of linear separators under bounded noise. In Proceedings of the 28th Conference on Learning Theory, Paris, France, vol\u00a040 (pp. 167\u2013190).","key":"6133_CR1"},{"unstructured":"Balcan, M., & Long, P. M. (2013). Active and passive learning of linear separators under log-concave distributions. In Proceedings of the 26th Annual Conference on Learning Theory, Princeton University, NJ, USA, vol\u00a030 (pp. 288\u2013316).","key":"6133_CR2"},{"key":"6133_CR3","first-page":"1205","volume":"7","author":"N Cesa-Bianchi","year":"2006","unstructured":"Cesa-Bianchi, N., Gentile, C., & Zaniboni, L. (2006). Worst-case analysis of selective sampling for linear classification. Journal of Machine Learning Research, 7, 1205\u20131230.","journal-title":"Journal of Machine Learning Research"},{"key":"6133_CR4","first-page":"551","volume":"7","author":"K Crammer","year":"2006","unstructured":"Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., & Singer, Y. (2006). Online passive-aggressive algorithms. J Mach Learn Res, 7, 551\u2013585.","journal-title":"J Mach Learn Res"},{"key":"6133_CR5","first-page":"1891","volume":"13","author":"K Crammer","year":"2012","unstructured":"Crammer, K., Dredze, M., & Pereira, F. (2012). Confidence-weighted linear classification for text categorization. Journal of Machine Learning Research, 13, 1891\u20131926.","journal-title":"Journal of Machine Learning Research"},{"issue":"2","key":"6133_CR6","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s10994-013-5327-x","volume":"91","author":"K Crammer","year":"2013","unstructured":"Crammer, K., Kulesza, A., & Dredze, M. (2013). Adaptive regularization of weight vectors. Machine Learning, 91(2), 155\u2013187.","journal-title":"Machine Learning"},{"issue":"7","key":"6133_CR7","doi-asserted-by":"publisher","first-page":"2558","DOI":"10.1016\/j.patcog.2014.02.001","volume":"47","author":"B Demir","year":"2014","unstructured":"Demir, B., & Bruzzone, L. (2014). A multiple criteria active learning method for support vector regression. Pattern Recognition, 47(7), 2558\u20132567.","journal-title":"Pattern Recognition"},{"issue":"1","key":"6133_CR8","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1109\/TCYB.2015.2496974","volume":"47","author":"B Du","year":"2017","unstructured":"Du, B., Wang, Z., Zhang, L., Zhang, L., Liu, W., Shen, J., & Tao, D. (2017). Exploring representativeness and informativeness for active learning. IEEE Transactions on Cybernetics, 47(1), 14\u201326.","journal-title":"IEEE Transactions on Cybernetics"},{"key":"6133_CR9","first-page":"2121","volume":"12","author":"JC Duchi","year":"2011","unstructured":"Duchi, J. C., Hazan, E., & Singer, Y. (2011). Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12, 2121\u20132159.","journal-title":"Journal of Machine Learning Research"},{"unstructured":"Golovin, D., Krause, A., & Ray, D. (2010). Near-optimal bayesian active learning with noisy observations. In Advances in Neural Information Processing Systems (pp. 766\u2013774).","key":"6133_CR10"},{"issue":"2\u20133","key":"6133_CR11","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1561\/2200000037","volume":"7","author":"S Hanneke","year":"2014","unstructured":"Hanneke, S. (2014). Theory of disagreement-based active learning. Foundations and Trends in Machine Learning, 7(2\u20133), 131\u2013309.","journal-title":"Foundations and Trends in Machine Learning"},{"issue":"7","key":"6133_CR12","doi-asserted-by":"publisher","first-page":"1338","DOI":"10.1109\/TKDE.2017.2778097","volume":"30","author":"S Hao","year":"2018","unstructured":"Hao, S., Lu, J., Zhao, P., Zhang, C., Hoi, S. C. H., & Miao, C. (2018). Second-order online active learning and its applications. IEEE Transactions on Knowledge and Data Engineering, 30(7), 1338\u20131351.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"2\u20133","key":"6133_CR13","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s10994-007-5016-8","volume":"69","author":"E Hazan","year":"2007","unstructured":"Hazan, E., Agarwal, A., & Kale, S. (2007). Logarithmic regret algorithms for online convex optimization. Machine Learning, 69(2\u20133), 169\u2013192.","journal-title":"Machine Learning"},{"issue":"2","key":"6133_CR14","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/s10994-012-5319-2","volume":"90","author":"SCH Hoi","year":"2013","unstructured":"Hoi, S. C. H., Jin, R., Zhao, P., & Yang, T. (2013). Online multiple kernel classification. Machine Learning, 90(2), 289\u2013316.","journal-title":"Machine Learning"},{"issue":"10","key":"6133_CR15","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1109\/TPAMI.2014.2307881","volume":"36","author":"S Huang","year":"2014","unstructured":"Huang, S., Jin, R., & Zhou, Z. (2014). Active learning by querying informative and representative examples. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(10), 1936\u20131949.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"2","key":"6133_CR16","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/s10844-008-0053-8","volume":"32","author":"I Katakis","year":"2009","unstructured":"Katakis, I., Tsoumakas, G., Banos, E., Bassiliades, N., & Vlahavas, I. P. (2009). An adaptive personalized news dissemination system. Journal of Intelligent Information Systems, 32(2), 191\u2013212.","journal-title":"Journal of Intelligent Information Systems"},{"key":"6133_CR17","first-page":"47:1","volume":"17","author":"J Lu","year":"2016","unstructured":"Lu, J., Hoi, S. C. H., Wang, J., Zhao, P., & Liu, Z. (2016a). Large scale online kernel learning. Journal of Machine Learning Research, 17, 47:1-47:43.","journal-title":"Journal of Machine Learning Research"},{"issue":"2","key":"6133_CR18","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/s10994-016-5555-y","volume":"103","author":"J Lu","year":"2016","unstructured":"Lu, J., Zhao, P., & Hoi, S. C. H. (2016). Online passive-aggressive active learning. Machine Learning, 103(2), 141\u2013183.","journal-title":"Machine Learning"},{"key":"6133_CR19","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.ins.2017.06.038","volume":"415","author":"E Lughofer","year":"2017","unstructured":"Lughofer, E. (2017). On-line active learning: A new paradigm to improve practical useability of data stream modeling methods. Information Sciences, 415, 356\u2013376.","journal-title":"Information Sciences"},{"unstructured":"Luo, H., Agarwal, A., Cesa-Bianchi, N., & Langford, J. (2016). Efficient second order online learning by sketching. In Advances in Neural Information Processing Systems (pp. 902\u2013910).","key":"6133_CR20"},{"doi-asserted-by":"crossref","unstructured":"Ma, J., Saul, L. K., Savage, S., & Voelker, G. M. (2009). Identifying suspicious urls: an application of large-scale online learning. In Proceedings of the 26th International Conference on Machine Learning, Montreal, Quebec, Canada (pp. 681\u2013688).","key":"6133_CR21","DOI":"10.1145\/1553374.1553462"},{"unstructured":"Settles, B. (2009). Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison.","key":"6133_CR22"},{"issue":"2","key":"6133_CR23","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1561\/2200000018","volume":"4","author":"S Shalev-Shwartz","year":"2012","unstructured":"Shalev-Shwartz, S. (2012). Online learning and online convex optimization. Foundations and Trends in Machine Learning, 4(2), 107\u2013194.","journal-title":"Foundations and Trends in Machine Learning"},{"issue":"1","key":"6133_CR24","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10107-010-0420-4","volume":"127","author":"S Shalev-Shwartz","year":"2011","unstructured":"Shalev-Shwartz, S., Singer, Y., Srebro, N., & Cotter, A. (2011). Pegasos: Primal estimated sub-gradient solver for SVM. Mathematical Programming, 127(1), 3\u201330.","journal-title":"Mathematical Programming"},{"issue":"5","key":"6133_CR25","doi-asserted-by":"publisher","first-page":"1068","DOI":"10.1109\/TNNLS.2016.2518223","volume":"28","author":"Q Song","year":"2017","unstructured":"Song, Q., Xu, Z., Fan, H., & Wang, D. (2017). Robust recurrent kernel online learning. IEEE Transactions on Neural Networks and Learning Systems, 28(5), 1068\u20131081.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"6","key":"6133_CR26","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1109\/TKDE.2016.2526675","volume":"28","author":"Y Sun","year":"2016","unstructured":"Sun, Y., Tang, K., Minku, L. L., Wang, S., & Yao, X. (2016). Online ensemble learning of data streams with gradually evolved classes. IEEE Transactions on Knowledge and Data Engineering, 28(6), 1532\u20131545.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"unstructured":"Tosh, C., & Dasgupta, S. (2017). Diameter-based active learning. In Proceedings of the 34th International Conference on Machine Learning, vol\u00a070 (pp. 3444\u20133452).","key":"6133_CR27"},{"issue":"3","key":"6133_CR28","first-page":"17:1","volume":"9","author":"Z Wang","year":"2015","unstructured":"Wang, Z., & Ye, J. (2015). Querying discriminative and representative samples for batch mode active learning. ACM Transactions on Knowledge Discovery from Data, 9(3), 17:1-17:23.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"issue":"5","key":"6133_CR29","doi-asserted-by":"publisher","first-page":"1242","DOI":"10.1007\/s10618-017-0500-7","volume":"31","author":"T Zhai","year":"2017","unstructured":"Zhai, T., Gao, Y., Wang, H., & Cao, L. (2017). Classification of high-dimensional evolving data streams via a resource-efficient online ensemble. Data Mining and Knowledge Discovery, 31(5), 1242\u20131265.","journal-title":"Data Mining and Knowledge Discovery"},{"issue":"7","key":"6133_CR30","doi-asserted-by":"publisher","first-page":"2079","DOI":"10.1109\/TNNLS.2018.2877433","volume":"30","author":"T Zhai","year":"2019","unstructured":"Zhai, T., Koriche, F., Wang, H., & Gao, Y. (2019). Tracking sparse linear classifiers. IEEE Transactions on Neural Networks and Learning Systems, 30(7), 2079\u20132092.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"unstructured":"Zhang, C. (2018). Efficient active learning of sparse halfspaces. In Proceeding of the 31st Conference on Learning Theory, Stockholm, Sweden, vol\u00a075 (pp. 1856\u20131880).","key":"6133_CR31"},{"doi-asserted-by":"crossref","unstructured":"Zhao, P., & Hoi, S. C. H. (2013). Cost-sensitive online active learning with application to malicious URL detection. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA (pp. 919\u2013927).","key":"6133_CR32","DOI":"10.1145\/2487575.2487647"},{"unstructured":"Zinkevich, M. (2003). Online convex programming and generalized infinitesimal gradient ascent. In Proceedings of the 20th International Conference on Machine Learning, Washington, DC, USA (pp. 928\u2013936).","key":"6133_CR33"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06133-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-022-06133-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-022-06133-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,11]],"date-time":"2023-03-11T01:05:06Z","timestamp":1678496706000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-022-06133-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,11]]},"references-count":33,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["6133"],"URL":"https:\/\/doi.org\/10.1007\/s10994-022-06133-8","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"type":"print","value":"0885-6125"},{"type":"electronic","value":"1573-0565"}],"subject":[],"published":{"date-parts":[[2022,3,11]]},"assertion":[{"value":"26 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 February 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The authors declare that the submitted work is original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":".","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code availability"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}