{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T22:03:20Z","timestamp":1743113000570,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030742508"},{"type":"electronic","value":"9783030742515"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-74251-5_18","type":"book-chapter","created":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T15:20:40Z","timestamp":1618240840000},"page":"222-234","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Performance Prediction for Hardware-Software Configurations: A Case Study for Video Games"],"prefix":"10.1007","author":[{"given":"Sven","family":"Peeters","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vitalik","family":"Melnikov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eyke","family":"H\u00fcllermeier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,13]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Arndt, O., L\u00fcders, M., Blume, H.: Statistical performance prediction for multicore applications based on scalability characteristics. In: ASAP 2019 (2019)","key":"18_CR1","DOI":"10.1109\/ASAP.2019.00015"},{"doi-asserted-by":"crossref","unstructured":"Brabham, D.C.: Crowdsourcing as a model for problem solving: an introduction and cases. Convergence 14, 75\u201390 (2008)","key":"18_CR2","DOI":"10.1177\/1354856507084420"},{"unstructured":"Cabannes, V., Rudi, A., Bach, F.: Structured prediction with partial labelling through the infimum loss. In: ICML (2020)","key":"18_CR3"},{"doi-asserted-by":"crossref","unstructured":"Cano, J., Guti\u00e9rrez, P., Krawczyk, B., Wozniak, M., Garc\u00eda, S.: Monotonic classification: an overview on algorithms, performance measures and data sets. Neurocomputing 341, 168\u2013182 (2019)","key":"18_CR4","DOI":"10.1016\/j.neucom.2019.02.024"},{"doi-asserted-by":"crossref","unstructured":"Foulds, J., Frank, E.: A review of multi-instance learning assumptions. In: The Knowledge Engineering Review (2010)","key":"18_CR5","DOI":"10.1017\/S026988890999035X"},{"doi-asserted-by":"crossref","unstructured":"Frazier, P.I.: A tutorial on Bayesian optimization. CoRR (2018)","key":"18_CR6","DOI":"10.1287\/educ.2018.0188"},{"unstructured":"Frenay, B., Verleysen, M.: Classification in the presence of label noise: a survey. IEEE Trans. Neural Netw. Learn. Syst. 25, 845\u2013869 (2014)","key":"18_CR7"},{"unstructured":"Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32, 675\u2013701 (1937)","key":"18_CR8"},{"unstructured":"Gupta, A., Shukla, N., Marla, L., Kolbeinsson, A., Yellepeddi, K.: How to incorporate monotonicity in deep networks while preserving flexibility? CoRR (2019)","key":"18_CR9"},{"key":"18_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/978-3-030-58285-2_5","volume-title":"KI 2020: Advances in Artificial Intelligence","author":"J Hanselle","year":"2020","unstructured":"Hanselle, J., Tornede, A., Wever, M., H\u00fcllermeier, E.: Hybrid ranking and regression for algorithm selection. In: Schmid, U., Kl\u00fcgl, F., Wolter, D. (eds.) KI 2020. LNCS (LNAI), vol. 12325, pp. 59\u201372. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58285-2_5"},{"doi-asserted-by":"crossref","unstructured":"H\u00fcllermeier, E., Cheng, W.: Superset learning based on generalized loss minimization. In: ECML\/PKDD 2015 (2015)","key":"18_CR11","DOI":"10.1007\/978-3-319-23525-7_16"},{"doi-asserted-by":"crossref","unstructured":"H\u00fcllermeier, E.: Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int. J. Appr. Reason. 55, 1519\u20131534 (2014)","key":"18_CR12","DOI":"10.1016\/j.ijar.2013.09.003"},{"unstructured":"Iglewicz, B., Hoaglin, D.: How to Detect and Handle Outliers. ASQC Basic References in Quality Control. ASQC Quality Press, Milwaukee (1993)","key":"18_CR13"},{"unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations (2015)","key":"18_CR14"},{"unstructured":"Nemenyi, P.: Distribution-free multiple comparisons. In: Biometrics (1962)","key":"18_CR15"},{"unstructured":"Sch\u00e4fer, D., H\u00fcllermeier, E.: Dyad ranking using Plackett-Luce models based on joint feature representations. Mach. Learn. 107, 903\u2013941 (2018)","key":"18_CR16"},{"doi-asserted-by":"crossref","unstructured":"Sch\u00f6lkopf, B., Smola, A.: Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge (2001)","key":"18_CR17","DOI":"10.7551\/mitpress\/4175.001.0001"},{"unstructured":"Settles, B.: Active learning literature survey. Technical report (2009)","key":"18_CR18"},{"doi-asserted-by":"crossref","unstructured":"Sharkawi, S., et al.: Performance projection of HPC applications using spec CFP 2006 benchmarks. In: 2009 IEEE International Symposium on Parallel Distributed Processing (2009)","key":"18_CR19","DOI":"10.1109\/IPDPS.2009.5161057"},{"unstructured":"Sill, J.: Monotonic networks. In: NIPS 10 (1997)","key":"18_CR20"},{"unstructured":"Sill, J., Abu-Mostafa, Y.S.: Monotonicity hints. In: NIPS 9 (1996)","key":"18_CR21"},{"unstructured":"Thereska, E., Doebel, B., Zheng, A.X., Nobel, P.: Practical performance models for complex, popular applications. SIGMETRICS Perform. Eval. Rev. 38, 1\u201312 (2010)","key":"18_CR22"},{"unstructured":"Wang, Y., Lee, V., Wei, G.Y., Brooks, D.: Predicting new workload or CPU performance by analyzing public datasets. ACM Trans. Archit. Code Optim. 15, 1\u201321 (2019)","key":"18_CR23"},{"unstructured":"You, S., Ding, D., Canini, K., Pfeifer, J., Gupta, M.: Deep lattice networks and partial monotonic functions. In: NIPS 30 (2017)","key":"18_CR24"},{"unstructured":"Zhou, Z.H.: A brief introduction to weakly supervised learning. Natl. Sci. Rev. 5, 44\u201353 (2017)","key":"18_CR25"},{"unstructured":"Zhu, X., Goldberg, A.B.: Introduction to semi-supervised learning. Synth. Lect. Artif. Intell. Mach. Learn. 3, 1\u2013130 (2009)","key":"18_CR26"}],"container-title":["Lecture Notes in Computer Science","Advances in Intelligent Data Analysis XIX"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-74251-5_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T04:40:18Z","timestamp":1724820018000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-74251-5_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030742508","9783030742515"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-74251-5_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"13 April 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Intelligent Data Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 April 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 April 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ida2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ida2021.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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"113","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":"35","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":"31% - 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":"4","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":"The conference was held online due to the COVID-19 pandemic","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}