{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:29:27Z","timestamp":1777703367073,"version":"3.51.4"},"reference-count":23,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T00:00:00Z","timestamp":1560384000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2019,7,9]]},"abstract":"<jats:p>\n                    \u00a0Applying semi-supervised learning to extreme learning machine (ELM), we propose a semi-supervised extreme learning machine classification framework (SSELM) with arbitrary norm (\n                    <jats:italic>q<\/jats:italic>\n                    -norm, q=0,1 and 2). However, the SSELM involves nonconvex and nonsmooth problem. In this work, two types of optimization methods are developed to solve the proposed SSELM. The first one is an exact solution approach that reformulates SSELM as mixed integer programming. The second is an approximation approach that approximates the SSELM framework by DC (difference of convex functions) programming. Several formulations for SSELM are presented with different norm. Furthermore, the proposed methods are applied in a practical medical dataset using near-infrared spectral technology. Experimental results in different spectral regions show that incorporating unlabeled samples in training improves the generalization compared with the supervised ELM when insufficient training information is available. Moreover, the proposed methods achieve equivalent performance in benchmark data sets compared to the supervised ELM algorithms and other semi-supervised methods. These results show the feasibility and effectiveness of the proposed algorithms.\n                  <\/jats:p>","DOI":"10.3233\/jifs-181501","type":"journal-article","created":{"date-parts":[[2019,6,18]],"date-time":"2019-06-18T12:22:29Z","timestamp":1560860549000},"page":"835-845","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Arbitrary norm semi-supervised extreme learning machine"],"prefix":"10.1177","volume":"37","author":[{"given":"Shibo","family":"Jing","sequence":"first","affiliation":[{"name":"College of Science, China Agricultural University, Beijing, 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Automation, Northwestern Polytechnical University, Xian, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liming","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Science, China Agricultural University, Beijing, 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Science, China Agricultural University, Beijing, 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,6,13]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2005.12.126"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2010.02.019"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.08.009"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2016.04.003"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2012.04.002"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2582746"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2012.12.004"},{"key":"e_1_3_2_9_2","first-page":"203","article-title":"Optimization Techniques for Semi-Supervised Support Vector Machines","volume":"9","author":"Chapelle O.","year":"2008","unstructured":"ChapelleO., SindhwaniV. and KeerthiS., Optimization Techniques for Semi-Supervised Support Vector Machines, Journal of Machine Learning Research 9 (2008), 203\u2013233.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_10_2","first-page":"1","article-title":"An overview on semi-supervised support vector machine","volume":"28","author":"Ding S.","year":"2015","unstructured":"DingS., ZhuZ. and ZhangX., An overview on semi-supervised support vector machine, Neural Computing & Applications 28 (2015), 1\u201310.","journal-title":"Neural Computing & Applications"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.01.073"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2010.12.043"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2014.2307349"},{"issue":"1","key":"e_1_3_2_14_2","article-title":"Convex analysis approach to DC programming: Theory, algorithms and applications","volume":"22","author":"Pham Dinh T.","year":"1997","unstructured":"Pham DinhT. and Le ThiH. A., Convex analysis approach to DC programming: Theory, algorithms and applications, Acta Mathematica Vietnamica 22(1) (1997).","journal-title":"Acta Mathematica Vietnamica"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_01002"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2014.11.031"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1162\/NECO_a_00673"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2018.04.003"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11634-013-0141-7"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/72.788640"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2011.06.026"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2005.10.010"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-68125-0_21"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.05.053"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-181501","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-181501","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-181501","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:38:34Z","timestamp":1777455514000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-181501"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,13]]},"references-count":23,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,7,9]]}},"alternative-id":["10.3233\/JIFS-181501"],"URL":"https:\/\/doi.org\/10.3233\/jifs-181501","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,13]]}}}