{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T15:16:46Z","timestamp":1743088606890,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030678319"},{"type":"electronic","value":"9783030678326"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/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":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-67832-6_20","type":"book-chapter","created":{"date-parts":[[2021,1,22]],"date-time":"2021-01-22T17:44:48Z","timestamp":1611337488000},"page":"240-252","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["DeepFusion: Deep Ensembles for Domain Independent System Fusion"],"prefix":"10.1007","author":[{"given":"Mihai Gabriel","family":"Constantin","sequence":"first","affiliation":[]},{"given":"Liviu-Daniel","family":"\u015etefan","sequence":"additional","affiliation":[]},{"given":"Bogdan","family":"Ionescu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,1,21]]},"reference":[{"issue":"2","key":"20_CR1","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157\u2013166 (1994)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"2","key":"20_CR2","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/BF00058655","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123\u2013140 (1996). https:\/\/doi.org\/10.1007\/BF00058655","journal-title":"Mach. Learn."},{"issue":"1","key":"20_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794. ACM (2016)","key":"20_CR4","DOI":"10.1145\/2939672.2939785"},{"unstructured":"Dellandr\u00e9a, E., Huigsloot, M., Chen, L., Baveye, Y., Xiao, Z., Sj\u00f6berg, M.: The mediaeval 2018 emotional impact of movies task. In: MediaEval (2018)","key":"20_CR5"},{"doi-asserted-by":"crossref","unstructured":"Deng, L., Platt, J.C.: Ensemble deep learning for speech recognition. In: Fifteenth Annual Conference of the International Speech Communication Association (2014)","key":"20_CR6","DOI":"10.21437\/Interspeech.2014-433"},{"doi-asserted-by":"crossref","unstructured":"Feichtenhofer, C., Pinz, A., Zisserman, A.: Convolutional two-stream network fusion for video action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1933\u20131941 (2016)","key":"20_CR7","DOI":"10.1109\/CVPR.2016.213"},{"issue":"771\u2013780","key":"20_CR8","first-page":"1612","volume":"14","author":"Y Freund","year":"1999","unstructured":"Freund, Y., Schapire, R., Abe, N.: A short introduction to boosting. J. Jpn. Soc. Artif. Intell. 14(771\u2013780), 1612 (1999)","journal-title":"J. Jpn. Soc. Artif. Intell."},{"key":"20_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1007\/3-540-59119-2_166","volume-title":"Computational Learning Theory","author":"Y Freund","year":"1995","unstructured":"Freund, Y., Schapire, R.E.: A desicion-theoretic generalization of on-line learning and an application to boosting. In: Vit\u00e1nyi, P. (ed.) EuroCOLT 1995. LNCS, vol. 904, pp. 23\u201337. Springer, Heidelberg (1995). https:\/\/doi.org\/10.1007\/3-540-59119-2_166"},{"key":"20_CR10","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189\u20131232 (2001)","journal-title":"Ann. Stat."},{"issue":"2","key":"20_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3054925","volume":"50","author":"HM Gomes","year":"2017","unstructured":"Gomes, H.M., Barddal, J.P., Enembreck, F., Bifet, A.: A survey on ensemble learning for data stream classification. ACM Comput. Surv. 50(2), 1\u201336 (2017)","journal-title":"ACM Comput. Surv."},{"unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)","key":"20_CR12"},{"issue":"3","key":"20_CR13","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1109\/34.667881","volume":"20","author":"J Kittler","year":"1998","unstructured":"Kittler, J., Hatef, M., Duin, R.P., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226\u2013239 (1998)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Kontschieder, P., Fiterau, M., Criminisi, A., Rota Bulo, S.: Deep neural decision forests. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1467\u20131475 (2015)","key":"20_CR14","DOI":"10.1109\/ICCV.2015.172"},{"unstructured":"Kougia, V., Pavlopoulos, J., Androutsopoulos, I.: AUEB NLP group at imageclefmed caption 2019. In: CEUR Workshop Proceedings, CLEF2019, pp. 09\u201312 (2019)","key":"20_CR15"},{"unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)","key":"20_CR16"},{"issue":"2","key":"20_CR17","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1023\/A:1022859003006","volume":"51","author":"LI Kuncheva","year":"2003","unstructured":"Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51(2), 181\u2013207 (2003)","journal-title":"Mach. Learn."},{"doi-asserted-by":"crossref","unstructured":"Li, X., Huo, Y., Jin, Q., Xu, J.: Detecting violence in video using subclasses. In: 2016 ACM Conference on Multimedia Conference, pp. 586\u2013590 (2016)","key":"20_CR18","DOI":"10.1145\/2964284.2967289"},{"doi-asserted-by":"crossref","unstructured":"Liu, L., et al.: Deep neural network ensembles against deception: Ensemble diversity, accuracy and robustness. arXiv preprint arXiv:1908.11091 (2019)","key":"20_CR19","DOI":"10.1109\/MASS.2019.00040"},{"unstructured":"Pelka, O., Friedrich, C.M., Garc\u00eda Seco de Herrera, A., M\u00fcller, H.: Overview of the imageclefmed 2019 concept detection task. CLEF working notes, CEUR (2019)","key":"20_CR20"},{"key":"20_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1007\/978-3-030-01364-6_20","volume-title":"Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis","author":"O Pelka","year":"2018","unstructured":"Pelka, O., Koitka, S., R\u00fcckert, J., Nensa, F., Friedrich, C.M.: Radiology Objects in COntext (ROCO): a multimodal image dataset. In: Stoyanov, D., et al. (eds.) LABELS\/CVII\/STENT -2018. LNCS, vol. 11043, pp. 180\u2013189. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01364-6_20"},{"issue":"3","key":"20_CR22","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1023\/A:1007618119488","volume":"42","author":"G R\u00e4tsch","year":"2001","unstructured":"R\u00e4tsch, G., Onoda, T., M\u00fcller, K.R.: Soft margins for AdaBoost. Mach. Learn. 42(3), 287\u2013320 (2001). https:\/\/doi.org\/10.1023\/A:1007618119488","journal-title":"Mach. Learn."},{"doi-asserted-by":"crossref","unstructured":"Roberts, R.J.: Pubmed central: The genbank of the published literature (2001)","key":"20_CR23","DOI":"10.1073\/pnas.98.2.381"},{"issue":"4","key":"20_CR24","doi-asserted-by":"crossref","first-page":"e1249","DOI":"10.1002\/widm.1249","volume":"8","author":"O Sagi","year":"2018","unstructured":"Sagi, O., Rokach, L.: Ensemble learning: a survey. Wiley Interdisc. Rev. Data Min Kowl. Discov. 8(4), e1249 (2018)","journal-title":"Wiley Interdisc. Rev. Data Min Kowl. Discov."},{"unstructured":"Sun, J.J., Liu, T., Prasad, G.: GLA in mediaeval 2018 emotional impact of movies task. In: Proceedings of the MediaEval 2018 Workshop (2018)","key":"20_CR25"},{"doi-asserted-by":"publisher","unstructured":"Wolpert, D.H.: The supervised learning no-free-lunch theorems. In: Roy, R.K., K\u00f6ppen, M., Ovaska, S., Furuhashi, T. (eds.) Soft Computing and Industry, pp. 25\u201342. Springer, London (2002). https:\/\/doi.org\/10.1007\/978-1-4471-0123-9_3","key":"20_CR26","DOI":"10.1007\/978-1-4471-0123-9_3"},{"unstructured":"Yi, Y., Wang, H., Li, Q.: CNN features for emotional impact of movies task. In: Proceedings of MediaEval 2018 Workshop (2018)","key":"20_CR27"},{"doi-asserted-by":"crossref","unstructured":"Zhou, Z.H., Feng, J.: Deep forest: towards an alternative to deep neural networks. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17, pp. 3553\u20133559 (2017)","key":"20_CR28","DOI":"10.24963\/ijcai.2017\/497"}],"container-title":["Lecture Notes in Computer Science","MultiMedia Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-67832-6_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T22:01:52Z","timestamp":1697666512000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-67832-6_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030678319","9783030678326"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-67832-6_20","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":"21 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MMM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Multimedia Modeling","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Prague","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Czech Republic","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":"22 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mmm2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mmm2021.cz\/","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":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"211","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":"73","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":"35% - 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":"2,63","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":"2,5","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)"}}]}}