{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T03:49:46Z","timestamp":1775274586794,"version":"3.50.1"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030041786","type":"print"},{"value":"9783030041793","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"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":[[2018]]},"DOI":"10.1007\/978-3-030-04179-3_59","type":"book-chapter","created":{"date-parts":[[2018,11,17]],"date-time":"2018-11-17T02:12:42Z","timestamp":1542420762000},"page":"668-679","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MCP Based Noise Resistant Algorithm for Training RBF Networks and Selecting Centers"],"prefix":"10.1007","author":[{"given":"Hao","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew Chi Sing","family":"Leung","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John","family":"Sum","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,11,18]]},"reference":[{"issue":"9","key":"59_CR1","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1109\/5.58326","volume":"78","author":"T Poggio","year":"1990","unstructured":"Poggio, T., Girosi, T.: Networks for approximation and learning. Proc. IEEE 78(9), 1481\u20131497 (1990)","journal-title":"Proc. IEEE"},{"key":"59_CR2","volume-title":"Neural Networks: A Comprehensive Foundation","author":"S Haykin","year":"1998","unstructured":"Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River (1998)"},{"issue":"2","key":"59_CR3","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1109\/72.839002","volume":"11","author":"J Gomm","year":"2000","unstructured":"Gomm, J., Yu, D.: Selecting radial basis function network centers with recursive orthogonal least squares training. IEEE Trans. Neural Netw. 11(2), 306\u2013314 (2000)","journal-title":"IEEE Trans. Neural Netw."},{"key":"59_CR4","unstructured":"Burr, J.B.: Digital neural network implementations. In: Neural Networks, Concepts, Applications, and Implementations, vol. 3, pp. 237\u2013285. Prentice Hall (1995)"},{"key":"59_CR5","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.neucom.2014.12.049","volume":"155","author":"Z Han","year":"2015","unstructured":"Han, Z., Feng, R., Wan, W.Y., Leung, C.S.: Online training and its convergence for faulty networks with multiplicative weight noise. Neurocomputing 155, 53\u201361 (2015)","journal-title":"Neurocomputing"},{"issue":"12","key":"59_CR6","doi-asserted-by":"publisher","first-page":"2941","DOI":"10.1162\/089976600300014782","volume":"12","author":"JL Bernier","year":"2000","unstructured":"Bernier, J.L., Ortega, J., Ros, E., Rojas, I., Prieto, A.: A quantitative study of fault tolerance, noise immunity, and generalization ability of MLPs. Neural Comput. 12(12), 2941\u20132964 (2000)","journal-title":"Neural Comput."},{"issue":"6","key":"59_CR7","doi-asserted-by":"publisher","first-page":"1360","DOI":"10.1109\/TNNLS.2016.2536172","volume":"28","author":"CS Leung","year":"2017","unstructured":"Leung, C.S., Wan, W.Y., Feng, R.: A regularizer approach for RBF networks under the concurrent weight failure situation. IEEE Trans. Neural Netw. Learn. Syst. 28(6), 1360\u20131372 (2017)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"2","key":"59_CR8","doi-asserted-by":"publisher","first-page":"894","DOI":"10.1214\/09-AOS729","volume":"38","author":"CH Zhang","year":"2010","unstructured":"Zhang, C.H.: Nearly unbiased variable selection under minimax concave penalty. Ann. Stat. 38(2), 894\u2013942 (2010)","journal-title":"Ann. Stat."},{"issue":"1","key":"59_CR9","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., Peleato, B., Eckstein, J.: 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."},{"issue":"1","key":"59_CR10","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1214\/10-AOAS388","volume":"5","author":"P Breheny","year":"2011","unstructured":"Breheny, P., Huang, J.: Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Ann. Appl. Stat. 5(1), 232\u2013253 (2011)","journal-title":"Ann. Appl. Stat."},{"issue":"3","key":"59_CR11","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1093\/biomet\/81.3.425","volume":"81","author":"DL Donoho","year":"1994","unstructured":"Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3), 425\u2013455 (1994)","journal-title":"Biometrika"},{"issue":"1\u20132","key":"59_CR12","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s10107-011-0484-9","volume":"137","author":"H Attouch","year":"2013","unstructured":"Attouch, H., Bolte, J., Svaiter, B.F.: Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized gauss-seidel methods. Math. Program. 137(1\u20132), 91\u2013129 (2013)","journal-title":"Math. Program."},{"key":"59_CR13","unstructured":"Wang, Y., Yin, W., Zeng, J.: Global convergence of ADMM in nonconvex nonsmooth optimization. J. Sci. Comput. (2015, accepted)"},{"key":"59_CR14","unstructured":"Lichman, M.: UCI machine learning repository (2013)"},{"issue":"8","key":"59_CR15","doi-asserted-by":"publisher","first-page":"1828","DOI":"10.1109\/TNNLS.2014.2377245","volume":"26","author":"Q Zhang","year":"2015","unstructured":"Zhang, Q., Hu, X., Zhang, B.: Comparison of $$l_1$$-norm SVR and sparse coding algorithms for linear regression. IEEE Trans. Neural Netw. Learn. Syst. 26(8), 1828\u20131833 (2015)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"59_CR16","doi-asserted-by":"crossref","unstructured":"Malioutov, D.M., Cetin, M., Willsky, A.S.: Homotopy continuation for sparse signal representation. In: Proceedings of the IEEE CASSP 2005, vol. 5, pp. 733\u2013736. IEEE Press, New York (2005)","DOI":"10.1109\/ICASSP.2005.1416408"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-04179-3_59","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T02:56:11Z","timestamp":1775271371000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-04179-3_59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030041786","9783030041793"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-04179-3_59","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"18 November 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Siem Reap","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambodia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 December 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 December 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conference.cs.cityu.edu.hk\/iconip\/","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":"575","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":"401","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":"70% - 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":"6","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)"}}]}}