{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T03:31:23Z","timestamp":1743132683135,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030029333"},{"type":"electronic","value":"9783030029340"}],"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\/978-3-030-02934-0_1","type":"book-chapter","created":{"date-parts":[[2018,11,19]],"date-time":"2018-11-19T05:42:53Z","timestamp":1542606173000},"page":"3-14","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Pareto-Based Many-Objective Convolutional Neural Networks"],"prefix":"10.1007","author":[{"given":"Hongjian","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Shixiong","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Jiaqi","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Dongjun","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Qiang","family":"Niu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,11,20]]},"reference":[{"issue":"9","key":"1_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/S0218001417550138","volume":"31","author":"S Bai","year":"2017","unstructured":"Bai, S.: Scene categorization through using objects represented by deep features. Int. J. Pattern Recogn. Artif. Intell. 31(9), 1\u201321 (2017)","journal-title":"Int. J. Pattern Recogn. Artif. Intell."},{"issue":"12","key":"1_CR2","doi-asserted-by":"publisher","first-page":"2212","DOI":"10.1109\/TNNLS.2014.2307532","volume":"25","author":"H Goh","year":"2014","unstructured":"Goh, H., Thome, N., Cord, M., Lim, J.H.: Learning deep hierarchical visual feature coding. IEEE Trans. Neural Netw. Learn. Syst. 25(12), 2212\u20132225 (2014)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"12","key":"1_CR3","doi-asserted-by":"publisher","first-page":"3263","DOI":"10.1109\/TNNLS.2015.2469673","volume":"26","author":"M Gong","year":"2015","unstructured":"Gong, M., Liu, J., Li, H., Cai, Q., Su, L.: A multiobjective sparse feature learning model for deep neural networks. IEEE Trans. Neural Netw. Learn. Syst. 26(12), 3263\u20133277 (2015)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"1_CR4","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep learning (2016). http:\/\/www.deeplearningbook.org. Book in preparation for MIT Press"},{"key":"1_CR5","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/978-3-319-07494-8_2","volume-title":"EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V","author":"VA Sosa Hern\u00e1ndez","year":"2014","unstructured":"Sosa Hern\u00e1ndez, V.A., Sch\u00fctze, O., Emmerich, M.: Hypervolume maximization via set based Newton\u2019s method. In: Tantar, A.-A., et al. (eds.) EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V. AISC, vol. 288, pp. 15\u201328. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-07494-8_2"},{"key":"1_CR6","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.neucom.2017.07.072","volume":"288","author":"Qiao Ke","year":"2018","unstructured":"Ke, Q., Zhang, J., Song, H., Wan, Y.: Big data analytics enabled by feature extraction based on partial independence. Neurocomputing 288, 3\u201310 (2018). Learning System in Real-time Machine Vision","journal-title":"Neurocomputing"},{"issue":"11","key":"1_CR7","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"1_CR8","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NIPS Workshop on Deep Learning and Unsupervised Feature Learning (2010)"},{"issue":"3","key":"1_CR9","first-page":"440","volume":"21","author":"A Trivedi","year":"2017","unstructured":"Trivedi, A., Srinivasan, D., Sanyal, K., Ghosh, A.: A survey of multi-objective evolutionary algorithms based on decomposition. IEEE Trans. Evol. Comput. 21(3), 440\u2013462 (2017)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., Lenc, K.: MatConvNet - convolutional neural networks for MATLAB. In: Proceeding of the ACM International Conference on Multimedia (2015)","DOI":"10.1145\/2733373.2807412"},{"issue":"4","key":"1_CR11","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1109\/TEVC.2014.2350987","volume":"19","author":"H Wang","year":"2015","unstructured":"Wang, H., Jiao, L., Yao, X.: Two$$\\_$$Arch2: an improved two-archive algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 19(4), 524\u2013541 (2015)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"6","key":"1_CR12","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1109\/TNNLS.2015.2512898","volume":"27","author":"C Xia","year":"2016","unstructured":"Xia, C., Qi, F., Shi, G.: Bottom-up visual saliency estimation with deep autoencoder-based sparse reconstruction. IEEE Trans. Neural Netw. Learn. Syst. 27(6), 1227\u20131240 (2016)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"09","key":"1_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/S0218001416600107","volume":"30","author":"Y Xia","year":"2016","unstructured":"Xia, Y., Zhang, B., Coenen, F.: Face occlusion detection using deep convolutional neural networks. Int. J. Pattern Recogn. Artif. Intell. 30(09), 1\u201324 (2016)","journal-title":"Int. J. Pattern Recogn. Artif. Intell."},{"issue":"6","key":"1_CR14","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","volume":"11","author":"Q Zhang","year":"2007","unstructured":"Zhang, Q., Li, H.: Moea\/d: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712\u2013731 (2007)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"1_CR15","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.ins.2016.05.026","volume":"367\u2013368","author":"J Zhao","year":"2016","unstructured":"Zhao, J., et al.: Multiobjective optimization of classifiers by means of 3D convex-hull-based evolutionary algorithms. Inf. Sci. 367\u2013368, 80\u2013104 (2016)","journal-title":"Inf. Sci."},{"key":"1_CR16","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1016\/j.asoc.2018.03.005","volume":"67","author":"J Zhao","year":"2018","unstructured":"Zhao, J.: 3D fast convex-hull-based evolutionary multiobjective optimization algorithm. Appl. Soft Comput. 67, 322\u2013336 (2018)","journal-title":"Appl. Soft Comput."},{"key":"1_CR17","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.dss.2018.05.003","volume":"111","author":"J Zhao","year":"2018","unstructured":"Zhao, J.: Multiobjective sparse ensemble learning by means of evolutionary algorithms. Decis. Support Syst. 111, 86\u2013100 (2018)","journal-title":"Decis. Support Syst."},{"key":"1_CR18","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.patcog.2016.05.028","volume":"61","author":"Z Zhao","year":"2017","unstructured":"Zhao, Z., Jiao, L., Zhao, J., Gu, J., Zhao, J.: Discriminant deep belief network for high-resolution SAR image classification. Pattern Recogn. 61, 686\u2013701 (2017)","journal-title":"Pattern Recogn."}],"container-title":["Lecture Notes in Computer Science","Web Information Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-02934-0_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:40:34Z","timestamp":1710268834000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-02934-0_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030029333","9783030029340"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-02934-0_1","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":"20 November 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taiyuan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"14 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wisa22018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/jisq.nju.edu.cn\/wisa2018\/index.html","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":"103","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":"29","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":"16","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":"28% - 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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}