{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T15:33:58Z","timestamp":1756308838236,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":22,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811518980"},{"type":"electronic","value":"9789811518997"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-981-15-1899-7_20","type":"book-chapter","created":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T16:02:38Z","timestamp":1574870558000},"page":"285-296","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Distributed Logistic Regression for Separated Massive Data"],"prefix":"10.1007","author":[{"given":"Peishen","family":"Shi","sequence":"first","affiliation":[]},{"given":"Puyu","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Hai","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,28]]},"reference":[{"key":"20_CR1","unstructured":"Mcdonald, R., Mohri, M., Silberman, N., Walker, D., Mann, G.: Efficient large-scale distributed training of conditional maximum entropy models. Advances in Neural Information Processing Systems, vol. 1, pp. 1231\u20131239. NIPS, La Jolla (2009)"},{"key":"20_CR2","unstructured":"McDonald, R., Hall, K., Mann, G.: Distributed training strategies for the structured perceptron. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 456\u2013464. ACL, Los Angeles (2010)"},{"issue":"1","key":"20_CR3","first-page":"3321","volume":"14","author":"Y Zhang","year":"2013","unstructured":"Zhang, Y., Duchi, J., Wainwright, M.: Communication-efficient algorithms for statistical optimization. J. Mach. Learn. Res. 14(1), 3321\u20133363 (2013)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"20_CR4","first-page":"592","volume":"30","author":"Y Zhang","year":"2013","unstructured":"Zhang, Y., Duchi, J., Wainwright, M.: Divide and conquer Kernel ridge regression: a distributed algorithm with minimax optimal rates. J. Mach. Learn. Res. 30(1), 592\u2013617 (2013)","journal-title":"J. Mach. Learn. Res."},{"issue":"10","key":"20_CR5","doi-asserted-by":"publisher","first-page":"5262","DOI":"10.1109\/TSP.2010.2055862","volume":"58","author":"G Mateos","year":"2010","unstructured":"Mateos, G., Bazerque, J., Giannakis, G.: Distributed sparse linear regression. IEEE Trans. Signal Process. 58(10), 5262\u20135276 (2010)","journal-title":"IEEE Trans. Signal Process."},{"issue":"11","key":"20_CR6","doi-asserted-by":"publisher","first-page":"1938","DOI":"10.1080\/02664763.2017.1401052","volume":"45","author":"P Wang","year":"2017","unstructured":"Wang, P., Zhang, H., Liang, Y.: Model selection with distributed SCAD penalty. J. Appl. Stat. 45(11), 1938\u20131955 (2017)","journal-title":"J. Appl. Stat."},{"key":"20_CR7","unstructured":"Wang J., Kolar M., Srebro N., Zhang T.: Efficient distributed learning with sparsity. In: International Conference on Machine Learning, vol. 70, pp. 3636\u20133645. PMLR, Sydney (2017)"},{"issue":"2","key":"20_CR8","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s00362-007-0078-z","volume":"50","author":"ML Menendez","year":"2009","unstructured":"Menendez, M.L., Pardo, L., Pardo, M.C.: Preliminary $$phi$$-divergence test estimators for linear restrictions in a logistic regression model. Stat. Pap. 50(2), 277\u2013300 (2009)","journal-title":"Stat. Pap."},{"issue":"1","key":"20_CR9","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s00362-005-0274-7","volume":"47","author":"JA Pardo","year":"2006","unstructured":"Pardo, J.A., Pardo, L., Pardo, M.C.: Minimum $$\\phi -$$divergence estimator in logistic regression models. Stat. Pap. 47(1), 91\u2013108 (2006)","journal-title":"Stat. Pap."},{"issue":"4","key":"20_CR10","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1007\/s00362-016-0780-9","volume":"57","author":"OM Revan","year":"2016","unstructured":"Revan, O.M.: Iterative algorithms of biased estimation methods in binary logistic regression. Stat. Pap. 57(4), 991\u20131016 (2016)","journal-title":"Stat. Pap."},{"issue":"1","key":"20_CR11","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/s00362-012-0488-4","volume":"55","author":"T Lange","year":"2015","unstructured":"Lange, T., Mosler, K., Mozharovskyi, P.: Fast nonparametric classification based on data depth. Stat. Pap. 55(1), 49\u201369 (2015)","journal-title":"Stat. Pap."},{"issue":"1","key":"20_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000016","volume":"3","author":"S Boyd","year":"2011","unstructured":"Boyd, S., Parikh, N., Chu, E.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1\u2013122 (2011)","journal-title":"Found. Trends Mach. Learn."},{"key":"20_CR13","unstructured":"Xie, P., Jin, K., Xing, E.: Distributed machine learning via sufficient factor broadcasting. Arxiv, \nhttp:\/\/arxiv.org\/abs\/1409.5705\n\n. Accessed 7 Sep 2015"},{"key":"20_CR14","unstructured":"Gopal, S., Yang, Y.: Distributed training of large-scale logistic models. In: Proceedings of the 30th International Conference on Machine Learning, vol. 28, pp. 289\u2013297. PMLR, Atlanta (2013)"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Peng, H., Liang, D., Choi, C.: Evaluating parallel logistic regression models. In: IEEE International Conference on Big Data, pp. 119\u2013126. IEEE, Silicon Valley (2013)","DOI":"10.1109\/BigData.2013.6691743"},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Kang, D., Lim, W., Shin, K.: Data\/feature distributed stochastic coordinate descent for logistic regression. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 1269\u20131278. ACM, Shanghai (2014)","DOI":"10.1145\/2661829.2662082"},{"issue":"1","key":"20_CR17","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/0898-1221(76)90003-1","volume":"2","author":"D Gabay","year":"1976","unstructured":"Gabay, D., Mercier, B.: A dual algorithm for the solution of nonlinear variational problems via finite element approximation. Comput. Math. Appl. 2(1), 17\u201340 (1976)","journal-title":"Comput. Math. Appl."},{"key":"20_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/978-3-662-38527-2_45","volume-title":"Optimization Techniques IFIP Technical Conference","author":"R Glowinski","year":"1974","unstructured":"Glowinski, R., Marroco, A.: On the solution of a class of non linear Dirichlet problems by a penalty-duality method and finite elements of order one. In: Marchuk, G.I. (ed.) Optimization Techniques IFIP Technical Conference. LNCS, pp. 327\u2013333. Springer, Berlin (1974). \nhttps:\/\/doi.org\/10.1007\/978-3-662-38527-2_45"},{"key":"20_CR19","volume-title":"Parallel and Distributed Computation: Numerical Methods","author":"D Bertsekas","year":"1997","unstructured":"Bertsekas, D., Tsitsiklis, J.: Parallel and Distributed Computation: Numerical Methods, 2nd edn. Athena Scientific, Belmont (1997)","edition":"2"},{"issue":"1","key":"20_CR20","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s10915-018-0757-z","volume":"78","author":"Y Wang","year":"2019","unstructured":"Wang, Y., Yin, W., Zeng, J.: Global convergence of ADMM in nonconvex nonsmooth optimization. J. Sci. Comput. 78(1), 29\u201363 (2019)","journal-title":"J. Sci. Comput."},{"issue":"12","key":"20_CR21","doi-asserted-by":"publisher","first-page":"6745","DOI":"10.1073\/pnas.96.12.6745","volume":"96","author":"U Alon","year":"1999","unstructured":"Alon, U., et al.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. U.S.A. 96(12), 6745\u20136750 (1999)","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"20_CR22","unstructured":"Blake, C., Merz, C.: UCI repository of machine learning databases (1998). \nhttp:\/\/www.ics.uci.edu\/~mlearn\/MLRepository.html"}],"container-title":["Communications in Computer and Information Science","Big Data"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-15-1899-7_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T21:04:54Z","timestamp":1574888694000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-15-1899-7_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9789811518980","9789811518997"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-981-15-1899-7_20","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"28 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BigData","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF Conference on Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuhan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bigdat2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/grid.hust.edu.cn\/bigdata2019","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"324","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":"30","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":"9% - 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":"3","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":"3","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}