{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T18:30:35Z","timestamp":1725993035022},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030014179"},{"type":"electronic","value":"9783030014186"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","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":[[2018]]},"DOI":"10.1007\/978-3-030-01418-6_75","type":"book-chapter","created":{"date-parts":[[2018,9,26]],"date-time":"2018-09-26T10:57:36Z","timestamp":1537959456000},"page":"771-781","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fast and Accurate Affect Prediction Using a Hierarchy of Random Forests"],"prefix":"10.1007","author":[{"given":"Maxime","family":"Sazadaly","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pierre","family":"Pinchon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arthur","family":"Fagot","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lionel","family":"Prevost","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Myriam Maumy","family":"Bertrand","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,9,27]]},"reference":[{"issue":"7","key":"75_CR1","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1016\/j.specom.2010.02.010","volume":"52","author":"D Bitouk","year":"2010","unstructured":"Bitouk, D., Verma, R., Nenkova, A.: Class-level spectral features for emotion recognition. Speech Commun. 52(7), 613\u2013625 (2010)","journal-title":"Speech Commun."},{"key":"75_CR2","unstructured":"Chang, J., Scherer, S.: Learning representations of emotional speech with deep convolutional generative adversarial networks. In: ICASSP, pp. 2746\u20132750, 2017"},{"key":"75_CR3","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, P.W.: SMOTE: synthetic minority oversampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"75_CR4","unstructured":"Drucker, H., Burges, C., Kaufman, L., Smola, A.J., Vapnik, V.: Support vector regression machines. In: Advances in Neural Information Processing Systems, pp. 155\u2013161 (1997)"},{"key":"75_CR5","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1002\/0470013494.ch3","volume-title":"Handbook of Cognition and Emotion","author":"Paul Ekman","year":"2005","unstructured":"Ekman, P.: Basic emotions. In: Handbook of Cognition and Emotion, pp. 45\u201360. Wiley, New York (1999)"},{"key":"75_CR6","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1007\/3-540-44795-4_13","volume-title":"Machine Learning: ECML 2001","author":"E Frank","year":"2001","unstructured":"Frank, E., Hall, M.: A simple approach to ordinal classification. In: De Raedt, L., Flach, P. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 145\u2013156. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-44795-4_13"},{"issue":"12","key":"75_CR7","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.1111\/j.1467-9280.2007.02024.x","volume":"18","author":"JR Fontaine","year":"2007","unstructured":"Fontaine, J.R., Scherer, K.R., Roesch, E.B., Ellsworth, P.C.: The world of emotions is not two-dimensional. Psychol. Sci. 18(12), 1050\u20131057 (2007)","journal-title":"Psychol. Sci."},{"issue":"2","key":"75_CR8","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1016\/j.concog.2008.03.019","volume":"17","author":"D Grandjean","year":"2008","unstructured":"Grandjean, D., Sander, D., Scherer, K.R.: Conscious emotional experience emerges as a function of multilevel, appraisal-driven response synchronization. Conscious. Cogn. 17(2), 484\u2013495 (2008)","journal-title":"Conscious. Cogn."},{"key":"75_CR9","doi-asserted-by":"crossref","unstructured":"Guo, G., Fu, Y., Wang, T.S., Dyer, C.R.: Locally adjusted robust regression for human age estimation. In: WACV (2008)","DOI":"10.1109\/WACV.2008.4544009"},{"key":"75_CR10","doi-asserted-by":"crossref","unstructured":"Han, J., Zhang, Z., Ringeval, F., Schuller, B.: Prediction-based learning for continuous emotion recognition in speech. In: ICASSP, pp. 5005\u20135009 (2017)","DOI":"10.1109\/ICASSP.2017.7953109"},{"key":"75_CR11","doi-asserted-by":"crossref","unstructured":"He, L., Jiang, D., Yang, L., Pei, E., Hu, P., Sahli, H.: Multimodal affective dimension prediction using deep bidirectional long short-term memory recurrent neural networks. In: AVEC, pp. 73\u201380 (2015)","DOI":"10.1145\/2808196.2811641"},{"issue":"7","key":"75_CR12","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton, G.E., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527\u20131554 (2006)","journal-title":"Neural Comput."},{"issue":"2","key":"75_CR13","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1109\/T-AFFC.2011.9","volume":"2","author":"MA Nicolaou","year":"2011","unstructured":"Nicolaou, M.A., Gunes, H., Pantic, M.: Continuous prediction of spontaneous affect from multiple cues and modalities in valence-arousal space. IEEE Trans. Affect. Comput. 2(2), 92\u2013105 (2011)","journal-title":"IEEE Trans. Affect. Comput."},{"issue":"2","key":"75_CR14","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/s10772-017-9396-2","volume":"20","author":"F Noroozi","year":"2017","unstructured":"Noroozi, F., Sapinski, T., Kaminska, D., Anbarjafari, G.: Vocal-based emotion recognition using random forests and decision tree. Int. J. Speech Technol. 20(2), 239\u2013246 (2017)","journal-title":"Int. J. Speech Technol."},{"key":"75_CR15","unstructured":"Qiao, X.: Noncrossing ordinal classification. arXiv:1505.03442 (2015)"},{"key":"75_CR16","doi-asserted-by":"crossref","unstructured":"Ringeval, F., et al.: AV+EC 2015: the first affect recognition challenge bridging across audio, video, and physiological data. In: AVEC, pp. 3\u20138 (2015)","DOI":"10.1145\/2808196.2811642"},{"issue":"6","key":"75_CR17","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1037\/h0077714","volume":"39","author":"J Russell","year":"1980","unstructured":"Russell, J.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161\u20131178 (1980)","journal-title":"J. Pers. Soc. Psychol."},{"key":"75_CR18","doi-asserted-by":"crossref","unstructured":"Sethu, V., Ambikairajah, E., Epps, J.: Empirical mode decomposition based weighted frequency feature for speech-based emotion classification. In: ICASSP, pp. 5017\u20135020 (2008)","DOI":"10.1109\/ICASSP.2008.4518785"},{"key":"75_CR19","doi-asserted-by":"crossref","unstructured":"Thukral, P., Mitra, K., Chellappa, R.: A hierarchical approach for human age estimation. In: ICASSP, pp. 1529\u20131532 (2012)","DOI":"10.1109\/ICASSP.2012.6288182"},{"key":"75_CR20","doi-asserted-by":"crossref","unstructured":"Valstar, M.F., et al.: AVEC 2014: 3D dimensional affect and depression recognition challenge. In: AVEC (2014)","DOI":"10.1145\/2661806.2661807"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2018"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01418-6_75","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,24]],"date-time":"2019-10-24T18:41:08Z","timestamp":1571942468000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-01418-6_75"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030014179","9783030014186"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01418-6_75","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rhodes","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"4 October 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2018","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":"icann2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2018\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"360","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"139","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"28","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"39% - 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"}},{"value":"2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"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"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"In addition there are 41 full poster papers and 11 short poster papers included in the proceedings","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}