{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:40:11Z","timestamp":1742924411143,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030109271"},{"type":"electronic","value":"9783030109288"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-10928-8_27","type":"book-chapter","created":{"date-parts":[[2019,1,24]],"date-time":"2019-01-24T08:19:39Z","timestamp":1548317979000},"page":"447-463","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Frame-Based Optimal Design"],"prefix":"10.1007","author":[{"given":"Sebastian","family":"Mair","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yannick","family":"Rudolph","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vanessa","family":"Closius","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ulf","family":"Brefeld","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,1,23]]},"reference":[{"issue":"3","key":"27_CR1","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1023\/B:JOCO.0000038913.96607.c2","volume":"8","author":"AA Ageev","year":"2004","unstructured":"Ageev, A.A., Sviridenko, M.I.: Pipage rounding: a new method of constructing algorithms with proven performance guarantee. J. Comb. Optim. 8(3), 307\u2013328 (2004)","journal-title":"J. Comb. Optim."},{"unstructured":"Allen-Zhu, Z., Li, Y., Singh, A., Wang, Y.: Near-optimal design of experiments via regret minimization. In: International Conference on Machine Learning, pp. 126\u2013135 (2017)","key":"27_CR2"},{"issue":"4","key":"27_CR3","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1137\/120867287","volume":"34","author":"H Avron","year":"2013","unstructured":"Avron, H., Boutsidis, C.: Faster subset selection for matrices and applications. SIAM J. Matrix Anal. Appl. 34(4), 1464\u20131499 (2013)","journal-title":"SIAM J. Matrix Anal. Appl."},{"issue":"4","key":"27_CR4","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1145\/235815.235821","volume":"22","author":"CB Barber","year":"1996","unstructured":"Barber, C.B., Dobkin, D.P., Huhdanpaa, H.: The quickhull algorithm for convex hulls. ACM Trans. Math. Softw. (TOMS) 22(4), 469\u2013483 (1996)","journal-title":"ACM Trans. Math. Softw. (TOMS)"},{"key":"27_CR5","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441","volume-title":"Convex Optimization","author":"S Boyd","year":"2004","unstructured":"Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, New York (2004)"},{"issue":"5","key":"27_CR6","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1002\/(SICI)1099-128X(199709\/10)11:5<393::AID-CEM483>3.0.CO;2-L","volume":"11","author":"R Bro","year":"1997","unstructured":"Bro, R., De Jong, S.: A fast non-negativity-constrained least squares algorithm. J. Chemom. 11(5), 393\u2013401 (1997)","journal-title":"J. Chemom."},{"unstructured":"Brooks, T.F., Pope, D.S., Marcolini, M.A.: Airfoil self-noise and prediction (1989)","key":"27_CR7"},{"unstructured":"Chaudhuri, K., Kakade, S.M., Netrapalli, P., Sanghavi, S.: Convergence rates of active learning for maximum likelihood estimation. In: Advances in Neural Information Processing Systems, pp. 1090\u20131098 (2015)","key":"27_CR8"},{"unstructured":"Derezi\u0144ski, M., Warmuth, M.K.: Subsampling for ridge regression via regularized volume sampling. arXiv preprint arXiv:1710.05110 (2017)","key":"27_CR9"},{"key":"27_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/11871842_61","volume-title":"Machine Learning: ECML 2006","author":"AN Dolia","year":"2006","unstructured":"Dolia, A.N., De Bie, T., Harris, C.J., Shawe-Taylor, J., Titterington, D.M.: The minimum volume covering ellipsoid estimation in kernel-defined feature spaces. In: F\u00fcrnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 630\u2013637. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11871842_61"},{"issue":"2","key":"27_CR11","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1016\/0377-2217(94)00366-1","volume":"92","author":"JH Dul\u00e1","year":"1996","unstructured":"Dul\u00e1, J.H., Helgason, R.V.: A new procedure for identifying the frame of the convex hull of a finite collection of points in multidimensional space. Eur. J. Oper. Res. 92(2), 352\u2013367 (1996)","journal-title":"Eur. J. Oper. Res."},{"issue":"4","key":"27_CR12","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.comgeo.2011.12.006","volume":"45","author":"JH Dul\u00e1","year":"2012","unstructured":"Dul\u00e1, J.H., L\u00f3pez, F.J.: Competing output-sensitive frame algorithms. Comput. Geom. 45(4), 186\u2013197 (2012)","journal-title":"Comput. Geom."},{"key":"27_CR13","volume-title":"Theory of Optimal Experiments","author":"VV Fedorov","year":"1972","unstructured":"Fedorov, V.V.: Theory of Optimal Experiments. Elsevier, New York (1972)"},{"issue":"2\u20133","key":"27_CR14","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1561\/2200000044","volume":"5","author":"A Kulesza","year":"2012","unstructured":"Kulesza, A., et al.: Determinantal point processes for machine learning. Found. Trends\u00ae Mach. Learn. 5(2\u20133), 123\u2013286 (2012)","journal-title":"Found. Trends\u00ae Mach. Learn."},{"doi-asserted-by":"crossref","unstructured":"Lawson, C.L., Hanson, R.J.: Solving least squares problems, vol. 15. SIAM (1995)","key":"27_CR15","DOI":"10.1137\/1.9781611971217"},{"unstructured":"Li, C., Jegelka, S., Sra, S.: Polynomial time algorithms for dual volume sampling. In: Advances in Neural Information Processing Systems, pp. 5045\u20135054 (2017)","key":"27_CR16"},{"issue":"2","key":"27_CR17","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s10852-005-1597-z","volume":"4","author":"FJ Lopez","year":"2005","unstructured":"Lopez, F.J.: Generating random points (or vectors) controlling the percentage of them that are extreme in their convex (or positive) hull. J. Math. Model. Algorithms 4(2), 219\u2013234 (2005)","journal-title":"J. Math. Model. Algorithms"},{"unstructured":"Mair, S., Boubekki, A., Brefeld, U.: Frame-based data factorizations. In: International Conference on Machine Learning, pp. 2305\u20132313 (2017)","key":"27_CR18"},{"unstructured":"Mariet, Z.E., Sra, S.: Elementary symmetric polynomials for optimal experimental design. In: Advances in Neural Information Processing Systems, pp. 2136\u20132145 (2017)","key":"27_CR19"},{"key":"27_CR20","series-title":"Springer Series in Operations Research and Financial Engineering","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-40065-5_18","volume-title":"Numerical Optimization","author":"J Nocedal","year":"2006","unstructured":"Nocedal, J., Wright, S.J.: Sequential quadratic programming. In: Nocedal, J., Wright, S.J. (eds.) Numerical Optimization. Springer Series in Operations Research and Financial Engineering. Springer, New York (2006). https:\/\/doi.org\/10.1007\/978-0-387-40065-5_18"},{"issue":"2","key":"27_CR21","first-page":"179","volume":"8","author":"T Ottmann","year":"2001","unstructured":"Ottmann, T., Schuierer, S., Soundaralakshmi, S.: Enumerating extreme points in higher dimensions. Nord. J. Comput. 8(2), 179\u2013192 (2001)","journal-title":"Nord. J. Comput."},{"issue":"3","key":"27_CR22","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/S0167-7152(96)00140-X","volume":"33","author":"RK Pace","year":"1997","unstructured":"Pace, R.K., Barry, R.: Sparse spatial autoregressions. Stat. Probab. Lett. 33(3), 291\u2013297 (1997)","journal-title":"Stat. Probab. Lett."},{"doi-asserted-by":"crossref","unstructured":"Pukelsheim, F.: Optimal Design of Experiments. SIAM (2006)","key":"27_CR23","DOI":"10.1137\/1.9780898719109"},{"key":"27_CR24","volume-title":"Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond","author":"B Sch\u00f6lkopf","year":"2002","unstructured":"Sch\u00f6lkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)"},{"issue":"4","key":"27_CR25","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1016\/S0893-6080(98)00032-X","volume":"11","author":"AJ Smola","year":"1998","unstructured":"Smola, A.J., Sch\u00f6lkopf, B., M\u00fcller, K.R.: The connection between regularization operators and support vector kernels. Neural Netw. 11(4), 637\u2013649 (1998)","journal-title":"Neural Netw."},{"issue":"3","key":"27_CR26","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s10994-009-5100-3","volume":"75","author":"M Sugiyama","year":"2009","unstructured":"Sugiyama, M., Nakajima, S.: Pool-based active learning in approximate linear regression. Mach. Learn. 75(3), 249\u2013274 (2009)","journal-title":"Mach. Learn."},{"key":"27_CR27","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Methodol.) 58, 267\u2013288 (1996)","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"issue":"143","key":"27_CR28","first-page":"1","volume":"18","author":"Y Wang","year":"2017","unstructured":"Wang, Y., Yu, A.W., Singh, A.: On computationally tractable selection of experiments in measurement-constrained regression models. J. Mach. Learn. Res. 18(143), 1\u201341 (2017)","journal-title":"J. Mach. Learn. Res."},{"issue":"12","key":"27_CR29","doi-asserted-by":"publisher","first-page":"1797","DOI":"10.1016\/S0008-8846(98)00165-3","volume":"28","author":"IC Yeh","year":"1998","unstructured":"Yeh, I.C.: Modeling of strength of high-performance concrete using artificial neural networks. Cem. Concr. Res. 28(12), 1797\u20131808 (1998)","journal-title":"Cem. Concr. Res."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-10928-8_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,14]],"date-time":"2024-07-14T08:51:16Z","timestamp":1720947076000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-10928-8_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030109271","9783030109288"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-10928-8_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 January 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dublin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ireland","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":"10 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ecmlpkdd2018.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"535","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":"131","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":"17","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":"24% - 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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}