{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T23:57:09Z","timestamp":1742947029766,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030830137"},{"type":"electronic","value":"9783030830144"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-83014-4_10","type":"book-chapter","created":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T16:03:12Z","timestamp":1626969792000},"page":"195-222","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Coreset-Based Data Compression for Logistic Regression"],"prefix":"10.1007","author":[{"given":"Nery","family":"Riquelme-Granada","sequence":"first","affiliation":[]},{"given":"Khuong An","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Zhiyuan","family":"Luo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,23]]},"reference":[{"key":"10_CR1","first-page":"2","volume":"17","author":"MR Ackermann","year":"2012","unstructured":"Ackermann, M.R., M\u00e4rtens, M., Raupach, C., Swierkot, K., Lammersen, C., Sohler, C.: StreamKM++: a clustering algorithm for data streams. J. Exp. Alg. (JEA) 17, 2\u20134 (2012)","journal-title":"J. Exp. Alg. (JEA)"},{"key":"10_CR2","first-page":"1","volume":"52","author":"PK Agarwal","year":"2005","unstructured":"Agarwal, P.K., Har-Peled, S., Varadarajan, K.R.: Geometric approximation via coresets. Comb. Comput. Geom. 52, 1\u201330 (2005)","journal-title":"Comb. Comput. Geom."},{"key":"10_CR3","unstructured":"Arthur, D., Vassilvitskii, S.: K-Means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027\u20131035. Society for Industrial and Applied Mathematics (2007)"},{"key":"10_CR4","unstructured":"Bachem, O., Lucic, M., Krause, A.: Practical coreset constructions for machine learning. arXiv preprint arXiv:1703.06476 (2017)"},{"issue":"1","key":"10_CR5","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.comgeo.2007.04.002","volume":"40","author":"M B\u0103doiu","year":"2008","unstructured":"B\u0103doiu, M., Clarkson, K.L.: Optimal core-sets for balls. Comput. Geom. 40(1), 14\u201322 (2008)","journal-title":"Comput. Geom."},{"key":"10_CR6","unstructured":"Braverman, V., Feldman, D., Lang, H.: New frameworks for offline and streaming coreset constructions. CoRR abs\/1612.00889 (2016). http:\/\/arxiv.org\/abs\/1612.00889"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Dasgupta, S., Gupta, A.: An elementary proof of the Johnson-Lindenstrauss lemma. International Computer Science Institute, Technical report 22(1), 1\u20135 (1999)","DOI":"10.1002\/rsa.10073"},{"key":"10_CR8","doi-asserted-by":"crossref","unstructured":"Davis, J., Goadrich, M.: The relationship between precision-recall and ROC curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233\u2013240 (2006)","DOI":"10.1145\/1143844.1143874"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Feldman, D., Langberg, M.: A unified framework for approximating and clustering data. In: Proceedings of the Forty-Third Annual ACM Symposium on Theory of Computing, pp. 569\u2013578. ACM (2011)","DOI":"10.1145\/1993636.1993712"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Feldman, D., Schmidt, M., Sohler, C.: Turning big data into tiny data: constant-size coresets for K-means, PCA and projective clustering. In: Proceedings of the Twenty-Fourth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1434\u20131453. SIAM (2013)","DOI":"10.1137\/1.9781611973105.103"},{"key":"10_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/978-3-540-31865-1_25","volume-title":"Advances in Information Retrieval","author":"C Goutte","year":"2005","unstructured":"Goutte, C., Gaussier, E.: A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: Losada, D.E., Fern\u00e1ndez-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 345\u2013359. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/978-3-540-31865-1_25"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Har-Peled, S., Mazumdar, S.: On coresets for k-Means and k-Median clustering. In: Proceedings of the Thirty-Sixth Annual ACM Symposium on Theory of Computing, pp. 291\u2013300. ACM (2004)","DOI":"10.1145\/1007352.1007400"},{"key":"10_CR13","unstructured":"Huggins, J., Campbell, T., Broderick, T.: Coresets for scalable Bayesian logistic regression. In: Advances in Neural Information Processing Systems, pp. 4080\u20134088 (2016)"},{"key":"10_CR14","unstructured":"Mustafa, N.H., Varadarajan, K.R.: Epsilon-approximations and epsilon-nets. arXiv preprint arXiv:1702.03676 (2017)"},{"key":"10_CR15","unstructured":"Phillips, J.M.: Coresets and sketches. arXiv preprint arXiv:1601.00617 (2016)"},{"key":"10_CR16","unstructured":"Reddi, S.J., P\u00f3czos, B., Smola, A.J.: Communication efficient coresets for empirical loss minimization. In: UAI, pp. 752\u2013761 (2015)"},{"key":"10_CR17","doi-asserted-by":"publisher","unstructured":"Riquelme-Granada, N., Nguyen., K.A., Luo., Z.: On generating efficient data summaries for logistic regression: a coreset-based approach. In: Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA, pp. 78\u201389. INSTICC. SciTePress (2020). https:\/\/doi.org\/10.5220\/0009823200780089","DOI":"10.5220\/0009823200780089"},{"key":"10_CR18","unstructured":"Riquelme-Granada, N., Nguyen, K., Luo, Z.: Coreset-based conformal prediction for large-scale learning. In: Conformal and Probabilistic Prediction and Applications, pp. 142\u2013162 (2019)"},{"key":"10_CR19","unstructured":"Riquelme-Granada, N., Nguyen, K.A., Luo, Z.: Fast probabilistic prediction for kernel SVM via enclosing balls. In: Conformal and Probabilistic Prediction and Applications, pp. 189\u2013208. PMLR (2020)"},{"key":"10_CR20","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107298019","volume-title":"Understanding Machine Learning: From Theory to Algorithms","author":"S Shalev-Shwartz","year":"2014","unstructured":"Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, New York (2014)"},{"issue":"2","key":"10_CR21","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1561\/2200000018","volume":"4","author":"S Shalev-Shwartz","year":"2012","unstructured":"Shalev-Shwartz, S., et al.: Online learning and online convex optimization. Found. Trends Machine Learn. 4(2), 107\u2013194 (2012)","journal-title":"Found. Trends Machine Learn."},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tangwongsan, K., Tirthapura, S.: Streaming k-means clustering with fast queries. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 449\u2013460. IEEE (2017)","DOI":"10.1109\/ICDE.2017.102"}],"container-title":["Communications in Computer and Information Science","Data Management Technologies and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-83014-4_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T16:05:59Z","timestamp":1626969959000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-83014-4_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030830137","9783030830144"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-83014-4_10","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"23 July 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DATA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Data Management Technologies and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 July 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 July 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"data2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.dataconference.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Primoris","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"70","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"20% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}