{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T16:09:40Z","timestamp":1744906180413,"version":"3.37.3"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2011,7,29]],"date-time":"2011-07-29T00:00:00Z","timestamp":1311897600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2012,3]]},"DOI":"10.1007\/s13042-011-0036-x","type":"journal-article","created":{"date-parts":[[2011,7,28]],"date-time":"2011-07-28T09:27:33Z","timestamp":1311845253000},"page":"59-70","source":"Crossref","is-referenced-by-count":4,"title":["Ordinal regression with continuous pairwise preferences"],"prefix":"10.1007","volume":"3","author":[{"given":"Maria","family":"Dobrska","sequence":"first","affiliation":[]},{"given":"Hui","family":"Wang","sequence":"additional","affiliation":[]},{"given":"William","family":"Blackburn","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2011,7,29]]},"reference":[{"key":"36_CR1","doi-asserted-by":"crossref","unstructured":"Burges C, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N, Hullender G (2005) Learning to rank using gradient descent. In: ICML\u201905: Proceedings of the 22nd international conference on machine learning. ACM, New York, NY, USA, pp 89\u201396","DOI":"10.1145\/1102351.1102363"},{"issue":"3","key":"36_CR2","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1162\/neco.2007.19.3.792","volume":"19","author":"W Chu","year":"2007","unstructured":"Chu W, Keerthi SS (2007) Support vector ordinal regression. Neural Comput 19(3):792\u2013815","journal-title":"Neural Comput"},{"key":"36_CR3","unstructured":"Cohen WW, Schapire RE, Singer Y (1998) Learning to order things. In: NIPS\u201997: Proceedings of the 1997 conference on advances in neural information processing systems 10. MIT Press, Cambridge, MA, USA, pp 451\u2013457"},{"key":"36_CR4","unstructured":"Crammer K, Singer Y (2001) Pranking with ranking. In: NIPS, pp 641\u2013647"},{"key":"36_CR5","doi-asserted-by":"crossref","unstructured":"Frank E, Hall M (2001) A simple approach to ordinal classification. In: EMCL\u201901: Proceedings of the 12th European conference on machine learning. Springer, London, UK, pp 145\u2013156","DOI":"10.1007\/3-540-44795-4_13"},{"key":"36_CR6","first-page":"933","volume":"4","author":"Y Freund","year":"2003","unstructured":"Freund Y, Iyer R, Schapire RE, Singer Y (2003) An efficient boosting algorithm for combining preferences. J Mach Learn Res 4:933\u2013969","journal-title":"J Mach Learn Res"},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Herbrich R, Graepel T, Obermayer K (1999) Support vector learning for ordinal regression. In: In international conference on artificial neural networks, pp 97\u2013102","DOI":"10.1049\/cp:19991091"},{"key":"36_CR8","doi-asserted-by":"crossref","unstructured":"Herbrich R, Graepel T, Obermayer K (2000) Large margin rank boundaries for ordinal regression. MIT Press, Cambridge, MA, pp 115\u2013132. http:\/\/citeseer.ist.psu.edu\/contextsummary\/1891774\/0","DOI":"10.7551\/mitpress\/1113.003.0010"},{"issue":"1","key":"36_CR9","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1504\/IJDMMM.2008.022537","volume":"1","author":"J Huhn","year":"2009","unstructured":"Huhn J, Hullermeier E (2009) Is an ordinal class structure useful in classifier learning?. Int J Data Min Model Manag 1(1):45\u201367","journal-title":"Int J Data Min Model Manag"},{"key":"36_CR10","doi-asserted-by":"crossref","unstructured":"Jiao T, Peng J, Terlaky T (2009) A confidence voting process for ranking problems based on support vector machines. Ann Oper Res 166(1):23\u201338. http:\/\/dx.doi.org\/10.1007\/s10479-008-0410-6","DOI":"10.1007\/s10479-008-0410-6"},{"key":"36_CR11","unstructured":"Rennie JD, Srebro N (2005) Loss functions for preference levels: regression with discrete ordered labels. In: Proceedings of the IJCAI multidisciplinary workshop on advances in preference handling, pp 180\u2013186"},{"key":"36_CR12","unstructured":"Shashua A, Levin A (2002) Ranking with large margin principle: two approaches. In: NIPS, pp 937\u2013944"},{"key":"36_CR13","unstructured":"Shevade SK, Chu W (2006) Minimum enclosing spheres formulations for support vector ordinal regression. In: ICDM\u201906: Proceedings of the sixth international conference on data mining. IEEE Computer Society, Washington, DC, USA, pp 1054\u20131058"},{"issue":"4","key":"36_CR14","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.ins.2005.07.007","volume":"176","author":"V Torra","year":"2006","unstructured":"Torra V, Domingo-Ferrer J, Mateo-Sanz JM, Ng M (2006) Regression for ordinal variables without underlying continuous variables. Inf Sci 176(4):465\u2013474","journal-title":"Inf Sci"},{"issue":"1","key":"36_CR15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patrec.2007.07.019","volume":"29","author":"W Waegeman","year":"2008","unstructured":"Waegeman W, Baets BD, Boullart L (2008) Roc analysis in ordinal regression learning. Pattern Recogn Lett 29(1):1\u20139","journal-title":"Pattern Recogn Lett"},{"key":"36_CR16","unstructured":"Witten IH, Frank E (2005) Data Mining: practical machine learning tools and techniques. Morgan Kaufmann, San Fransisco"},{"issue":"5","key":"36_CR17","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1109\/TNN.2009.2017533","volume":"20","author":"B Zhao","year":"2009","unstructured":"Zhao B, Wang F, Zhang C (2009) Block-quantized support vector ordinal regression. IEEE Trans Neural Netw 20(5):882\u201390","journal-title":"IEEE Trans Neural Netw"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-011-0036-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13042-011-0036-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-011-0036-x","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T09:52:52Z","timestamp":1712656372000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13042-011-0036-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,7,29]]},"references-count":17,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2012,3]]}},"alternative-id":["36"],"URL":"https:\/\/doi.org\/10.1007\/s13042-011-0036-x","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2011,7,29]]}}}