{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T08:40:08Z","timestamp":1742978408398,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030645793"},{"type":"electronic","value":"9783030645809"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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":[[2020]]},"DOI":"10.1007\/978-3-030-64580-9_1","type":"book-chapter","created":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T18:17:02Z","timestamp":1609957022000},"page":"1-11","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-kernel Covariance Terms in Multi-output Support Vector Machines"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5320-0212","authenticated-orcid":false,"given":"Elisa","family":"Marcelli","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9783-608X","authenticated-orcid":false,"given":"Renato","family":"De Leone","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,7]]},"reference":[{"key":"1_CR1","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1007\/978-3-540-45167-9_41","volume-title":"Learning Theory and Kernel Machines","author":"S Ben-David","year":"2003","unstructured":"Ben-David, S., Schuller, R.: Exploiting task relatedness for multiple task learning. In: Sch\u00f6lkopf, B., Warmuth, M.K. (eds.) COLT-Kernel 2003. LNCS (LNAI), vol. 2777, pp. 567\u2013580. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/978-3-540-45167-9_41"},{"issue":"6","key":"1_CR2","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1016\/j.ijar.2011.01.007","volume":"52","author":"C Bielza","year":"2011","unstructured":"Bielza, C., Li, G., Larranaga, P.: Multi-dimensional classification with Bayesian networks. Int. J. Approximate Reasoning 52(6), 705\u2013727 (2011)","journal-title":"Int. J. Approximate Reasoning"},{"issue":"5","key":"1_CR3","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1002\/widm.1157","volume":"5","author":"H Borchani","year":"2015","unstructured":"Borchani, H., Varando, G., Bielza, C., Larra\u00f1aga, P.: A survey on multi-output regression. Wiley Interdisci. Rev. Data Min. Knowl. Disc. 5(5), 216\u2013233 (2015)","journal-title":"Wiley Interdisci. Rev. Data Min. Knowl. Disc."},{"issue":"6","key":"1_CR4","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1109\/TNNLS.2012.2187307","volume":"23","author":"F Cai","year":"2012","unstructured":"Cai, F., Cherkassky, V.: Generalized SMO algorithm for SVM-based multitask learning. IEEE Trans. Neural Netw. Learn. Syst. 23(6), 997\u20131003 (2012)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"5","key":"1_CR5","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1111\/j.1365-3121.1992.tb00605.x","volume":"4","author":"N Cressie","year":"1992","unstructured":"Cressie, N.: Statistics for spatial data. Terra Nova 4(5), 613\u2013617 (1992)","journal-title":"Terra Nova"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Evgeniou, T., Pontil, M.: Regularized multi-task learning. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 109\u2013117 (2004)","DOI":"10.1145\/1014052.1014067"},{"key":"1_CR7","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.ins.2017.06.017","volume":"415","author":"G Melki","year":"2017","unstructured":"Melki, G., Cano, A., Kecman, V., Ventura, S.: Multi-target support vector regression via correlation regressor chains. Inf. Sci. 415, 53\u201369 (2017)","journal-title":"Inf. Sci."},{"key":"1_CR8","unstructured":"Melkumyan, A., Ramos, F.: Multi-kernel gaussian processes. In: Twenty-Second International Joint Conference on Artificial Intelligence (2011)"},{"key":"1_CR9","unstructured":"Micchelli, C.A., Pontil, M.: Kernels for multi-task learning. In: Advances in neural Information Processing Systems, pp. 921\u2013928 (2005)"},{"key":"1_CR10","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-3-642-14125-6_3","volume-title":"Preference Learning","author":"S Vembu","year":"2010","unstructured":"Vembu, S., G\u00e4rtner, T.: Label ranking algorithms: a survey. In: F\u00fcrnkranz, J., H\u00fcllermeier, E. (eds.) Preference Learning, pp. 45\u201364. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-14125-6_3"},{"key":"1_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-05294-5","volume-title":"Multivariate Geostatistics: An Introduction with Applications","author":"H Wackernagel","year":"2013","unstructured":"Wackernagel, H.: Multivariate Geostatistics: An Introduction with Applications. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-662-05294-5"},{"issue":"7","key":"1_CR12","first-page":"2409","volume":"31","author":"D Xu","year":"2019","unstructured":"Xu, D., Shi, Y., Tsang, I.W., Ong, Y.S., Gong, C., Shen, X.: Survey on multi-output learning. IEEE Trans. Neural Networks Learn. Syst. 31(7), 2409\u20132429 (2019)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"issue":"2","key":"1_CR13","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1007\/s11042-013-1526-5","volume":"71","author":"S Xu","year":"2013","unstructured":"Xu, S., An, X., Qiao, X., Zhu, L.: Multi-task least-squares support vector machines. Multimedia Tools Appl. 71(2), 699\u2013715 (2013). https:\/\/doi.org\/10.1007\/s11042-013-1526-5","journal-title":"Multimedia Tools Appl."},{"key":"1_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1007\/978-3-319-93701-4_32","volume-title":"Computational Science \u2013 ICCS 2018","author":"J Zhang","year":"2018","unstructured":"Zhang, J., He, Y., Tang, J.: Multi-view multi-task support vector machine. In: Shi, Y., et al. (eds.) ICCS 2018. LNCS, vol. 10861, pp. 419\u2013428. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93701-4_32"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-64580-9_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T18:17:51Z","timestamp":1609957071000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-64580-9_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030645793","9783030645809"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-64580-9_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"7 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Siena","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 July 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mod2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2020.icas.xyz\/","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":"in-house system and easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"209","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":"116","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":"56% - 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":"5-6","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":"1-2","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)"}}]}}