{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:18:58Z","timestamp":1750220338168,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":27,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,12,4]],"date-time":"2021-12-04T00:00:00Z","timestamp":1638576000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Research on the health status evaluation method of aircraft structure with multiple sources of data based on artificial intelligence and data mining technology","award":["15903-412312-20250"],"award-info":[{"award-number":["15903-412312-20250"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,12,4]]},"DOI":"10.1145\/3507548.3507580","type":"proceedings-article","created":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T23:12:40Z","timestamp":1646867560000},"page":"209-215","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Regression Algorithm Based on Self-Distillation and Ensemble Learning"],"prefix":"10.1145","author":[{"given":"Yaqi","family":"Li","sequence":"first","affiliation":[{"name":"School of Data Science&Engineering, East China Normal University, China"}]},{"given":"Qiwen","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Data Science&Engineering, East China Normal University, China"}]},{"given":"Gang","family":"Liu","sequence":"additional","affiliation":[{"name":"China Academy of Launch Vehicle Technology, China"}]}],"member":"320","published-online":{"date-parts":[[2022,3,9]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"A tutorial on support vector regression. Statistics and computing, 14, 3 (August","author":"Smola Alex J","year":"2004","unstructured":"Smola , Alex J and Sch\u00f6lkopf , Bernhard . 2004. A tutorial on support vector regression. Statistics and computing, 14, 3 (August 2004 ), 199-222. https:\/\/doi.org\/10.1023\/b:stco.0000035301.49549.88 10.1023\/b:stco.0000035301.49549.88 Smola, Alex J and Sch\u00f6lkopf, Bernhard. 2004. A tutorial on support vector regression. Statistics and computing, 14, 3 (August 2004), 199-222. https:\/\/doi.org\/10.1023\/b:stco.0000035301.49549.88"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1006\/jcss.1997.1504"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1013203451"},{"key":"e_1_3_2_1_5_1","first-page":"2","article-title":"Deep residual learning for nonlinear regression","volume":"22","author":"Chen Dongwei","year":"2020","unstructured":"Chen , Dongwei , Hu , Fei , Nian , Guokui and Yang , Tiantian . 2020 . Deep residual learning for nonlinear regression . Entropy , 22 , 2 (February 2020), 193. https:\/\/doi.org\/10.3390\/e22020193 10.3390\/e22020193 Chen, Dongwei, Hu, Fei, Nian, Guokui and Yang, Tiantian. 2020. Deep residual learning for nonlinear regression. Entropy, 22, 2 (February 2020), 193. https:\/\/doi.org\/10.3390\/e22020193","journal-title":"Entropy"},{"key":"e_1_3_2_1_6_1","volume-title":"Gradient boosting neural networks: Grownet. arXiv preprint arXiv:2002.07971","author":"Badirli Sarkhan","year":"2020","unstructured":"Badirli , Sarkhan , Liu , Xuanqing , Xing , Zhengming , Bhowmik , Avradeep , Doan , Khoa and Keerthi , Sathiya S . 2020. Gradient boosting neural networks: Grownet. arXiv preprint arXiv:2002.07971 ( 2020 ). Badirli, Sarkhan, Liu, Xuanqing, Xing, Zhengming, Bhowmik, Avradeep, Doan, Khoa and Keerthi, Sathiya S. 2020. Gradient boosting neural networks: Grownet. arXiv preprint arXiv:2002.07971 (2020)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3003748"},{"key":"e_1_3_2_1_8_1","volume-title":"Sequential training of neural networks with gradient boosting. arXiv preprint arXiv:1909.12098","author":"Mart\u0131nez-Munoz Gonzalo","year":"2019","unstructured":"Mart\u0131nez-Munoz , Gonzalo and Superior , Escuela Polit\u00e9ctica . 2019. Sequential training of neural networks with gradient boosting. arXiv preprint arXiv:1909.12098 ( 2019 ). Mart\u0131nez-Munoz, Gonzalo and Superior, Escuela Polit\u00e9ctica. 2019. Sequential training of neural networks with gradient boosting. arXiv preprint arXiv:1909.12098 (2019)."},{"volume-title":"Proceedings of International Conference on Machine Learning, 1607-1616","author":"Furlanello Tommaso","key":"e_1_3_2_1_9_1","unstructured":"Furlanello , Tommaso , Lipton , Zachary , Tschannen , Michael , Itti , Laurent and Anandkumar , Anima . Born again neural networks. 2018 . In Proceedings of International Conference on Machine Learning, 1607-1616 . Furlanello, Tommaso, Lipton, Zachary, Tschannen, Michael, Itti, Laurent and Anandkumar, Anima. Born again neural networks. 2018. In Proceedings of International Conference on Machine Learning, 1607-1616."},{"key":"e_1_3_2_1_10_1","volume-title":"Kevin Kam Fung","author":"Yuen","year":"2017","unstructured":"Yuen , Kevin Kam Fung . 2017 . Towards multiple regression analyses for relationships of air quality and weather. Journal of Advances in Information Technology Vol , 8, 2 (May 2017), 135-140. 10.12720\/jait.8.2.135-140 Yuen, Kevin Kam Fung. 2017. Towards multiple regression analyses for relationships of air quality and weather. Journal of Advances in Information Technology Vol, 8, 2 (May 2017), 135-140. 10.12720\/jait.8.2.135-140"},{"key":"e_1_3_2_1_11_1","volume-title":"Meteorology visibility estimation by using multi-support vector regression method. Journal of Advances in Information Technology Vol, 11, 2 (May","author":"Lo Wai Lun","year":"2020","unstructured":"Lo , Wai Lun , Zhu , Meimei and Fu , Hong . 2020. Meteorology visibility estimation by using multi-support vector regression method. Journal of Advances in Information Technology Vol, 11, 2 (May 2020 ), 40-47. 10.12720\/jait.11.2.40-47 Lo, Wai Lun, Zhu, Meimei and Fu, Hong. 2020. Meteorology visibility estimation by using multi-support vector regression method. Journal of Advances in Information Technology Vol, 11, 2 (May 2020), 40-47. 10.12720\/jait.11.2.40-47"},{"key":"e_1_3_2_1_12_1","volume-title":"Comparison of statistical logistic regression and random forest machine learning techniques in predicting diabetes. Journal of Advances in Information Technology Vol, 11, 2 (May","author":"Daghistani Tahani","year":"2020","unstructured":"Daghistani , Tahani and Alshammari , Riyad . 2020. Comparison of statistical logistic regression and random forest machine learning techniques in predicting diabetes. Journal of Advances in Information Technology Vol, 11, 2 (May 2020 ), 78-83. 10.12720\/jait.11.2.78-83 Daghistani, Tahani and Alshammari, Riyad. 2020. Comparison of statistical logistic regression and random forest machine learning techniques in predicting diabetes. Journal of Advances in Information Technology Vol, 11, 2 (May 2020), 78-83. 10.12720\/jait.11.2.78-83"},{"volume-title":"Proceedings of Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778","author":"He Kaiming","key":"e_1_3_2_1_13_1","unstructured":"He , Kaiming , Zhang , Xiangyu , Ren , Shaoqing and Sun , Jian . Deep residual learning for image recognition. 2016 . In Proceedings of Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778 . He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing and Sun, Jian. Deep residual learning for image recognition. 2016. In Proceedings of Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778."},{"key":"e_1_3_2_1_14_1","volume-title":"Online deep learning: Learning deep neural networks on the fly. arXiv preprint arXiv:1711.03705 (July","author":"Sahoo Doyen","year":"2017","unstructured":"Sahoo , Doyen , Pham , Quang , Lu , Jing and Hoi , Steven CH . 2017. Online deep learning: Learning deep neural networks on the fly. arXiv preprint arXiv:1711.03705 (July 2017 ). https:\/\/doi.org\/10.24963\/ijcai.2018\/369 10.24963\/ijcai.2018 Sahoo, Doyen, Pham, Quang, Lu, Jing and Hoi, Steven CH. 2017. Online deep learning: Learning deep neural networks on the fly. arXiv preprint arXiv:1711.03705 (July 2017). https:\/\/doi.org\/10.24963\/ijcai.2018\/369"},{"key":"e_1_3_2_1_15_1","volume-title":"Online deep learning based on auto-encoder. Applied Intelligence","author":"Zhang Liu","year":"2021","unstructured":"Zhang , Si-si, Liu , Jian-wei, Zuo , Xin , Lu , Run-kun and Lian , Si-ming. 2021. Online deep learning based on auto-encoder. Applied Intelligence ( 2021 ), 1-20. Zhang, Si-si, Liu, Jian-wei, Zuo, Xin, Lu, Run-kun and Lian, Si-ming. 2021. Online deep learning based on auto-encoder. Applied Intelligence (2021), 1-20."},{"key":"e_1_3_2_1_16_1","volume-title":"Peter.","author":"Hansen","year":"1990","unstructured":"Hansen , Lars Kai and Salamon , Peter. 1990 . Neural network ensembles. IEEE transactions on pattern analysis and machine intelligence, 12, 10 (1990), 993-1001. Hansen, Lars Kai and Salamon, Peter. 1990. Neural network ensembles. IEEE transactions on pattern analysis and machine intelligence, 12, 10 (1990), 993-1001."},{"key":"e_1_3_2_1_17_1","volume-title":"Ensemble deep learning: A review. arXiv preprint arXiv:2104.02395","author":"Ganaie MA","year":"2021","unstructured":"Ganaie , MA and Hu , Minghui . 2021. Ensemble deep learning: A review. arXiv preprint arXiv:2104.02395 ( 2021 ). Ganaie, MA and Hu, Minghui. 2021. Ensemble deep learning: A review. arXiv preprint arXiv:2104.02395 (2021)."},{"key":"e_1_3_2_1_18_1","volume-title":"December 19, 2018 from https:\/\/machinelearningmastery.com\/ensemble-methods-for-deep-learning-neural-networks\/","author":"Brownlee Jason","year":"2018","unstructured":"Brownlee , Jason . 2018 . Ensemble Learning Methods for Deep Learning Neural Networks . December 19, 2018 from https:\/\/machinelearningmastery.com\/ensemble-methods-for-deep-learning-neural-networks\/ Brownlee, Jason. 2018. Ensemble Learning Methods for Deep Learning Neural Networks. December 19, 2018 from https:\/\/machinelearningmastery.com\/ensemble-methods-for-deep-learning-neural-networks\/"},{"key":"e_1_3_2_1_19_1","volume-title":"Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531","author":"Hinton Geoffrey","year":"2015","unstructured":"Hinton , Geoffrey , Vinyals , Oriol and Dean , Jeff . 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 ( 2015 ). Hinton, Geoffrey, Vinyals, Oriol and Dean, Jeff. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)."},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 10687-10698","author":"Xie Qizhe","year":"2020","unstructured":"Xie , Qizhe , Luong , Minh-Thang , Hovy , Eduard and Le , Quoc V . Self-training with noisy student improves imagenet classification. 2020 . In Proceedings of Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 10687-10698 . https:\/\/doi.org\/10.1109\/cvpr42600. 2020 .01070 10.1109\/cvpr42600.2020.01070 Xie, Qizhe, Luong, Minh-Thang, Hovy, Eduard and Le, Quoc V. Self-training with noisy student improves imagenet classification. 2020. In Proceedings of Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 10687-10698. https:\/\/doi.org\/10.1109\/cvpr42600.2020.01070"},{"key":"e_1_3_2_1_21_1","volume-title":"Proceedings of Proceedings of the IEEE\/CVF International Conference on Computer Vision, 4794-4802","author":"Cho Jang Hyun","year":"2019","unstructured":"Cho , Jang Hyun and Hariharan , Bharath . On the efficacy of knowledge distillation. 2019 . In Proceedings of Proceedings of the IEEE\/CVF International Conference on Computer Vision, 4794-4802 . https:\/\/doi.org\/10.1109\/iccv. 2019 .00489 10.1109\/iccv.2019.00489 Cho, Jang Hyun and Hariharan, Bharath. On the efficacy of knowledge distillation. 2019. In Proceedings of Proceedings of the IEEE\/CVF International Conference on Computer Vision, 4794-4802. https:\/\/doi.org\/10.1109\/iccv.2019.00489"},{"volume-title":"Proceedings of Proceedings of the AAAI Conference on Artificial Intelligence, 5628-5635","author":"Yang Chenglin","key":"e_1_3_2_1_22_1","unstructured":"Yang , Chenglin , Xie , Lingxi , Qiao , Siyuan and Yuille , Alan L . Training deep neural networks in generations: A more tolerant teacher educates better students. 2019 . In Proceedings of Proceedings of the AAAI Conference on Artificial Intelligence, 5628-5635 . https:\/\/doi.org\/10.1609\/aaai.v33i01.33015628 10.1609\/aaai.v33i01.33015628 Yang, Chenglin, Xie, Lingxi, Qiao, Siyuan and Yuille, Alan L. Training deep neural networks in generations: A more tolerant teacher educates better students. 2019. In Proceedings of Proceedings of the AAAI Conference on Artificial Intelligence, 5628-5635. https:\/\/doi.org\/10.1609\/aaai.v33i01.33015628"},{"key":"e_1_3_2_1_23_1","volume-title":"Learning efficient object detection models with knowledge distillation. Advances in neural information processing systems, 30","author":"Chen Guobin","year":"2017","unstructured":"Chen , Guobin , Choi , Wongun , Yu , Xiang , Han , Tony and Chandraker , Manmohan . 2017. Learning efficient object detection models with knowledge distillation. Advances in neural information processing systems, 30 ( 2017 ). Chen, Guobin, Choi, Wongun, Yu, Xiang, Han, Tony and Chandraker, Manmohan. 2017. Learning efficient object detection models with knowledge distillation. Advances in neural information processing systems, 30 (2017)."},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of Proceedings of the IEEE\/CVF International Conference on Computer Vision, 263-272","author":"Saputra Muhamad Risqi U","year":"2019","unstructured":"Saputra , Muhamad Risqi U , De Gusmao , Pedro PB, Almalioglu , Yasin , Markham , Andrew and Trigoni , Niki . Distilling knowledge from a deep pose regressor network. 2019 . In Proceedings of Proceedings of the IEEE\/CVF International Conference on Computer Vision, 263-272 . https:\/\/doi.org\/10.1109\/iccv. 2019 .00035 10.1109\/iccv.2019.00035 Saputra, Muhamad Risqi U, De Gusmao, Pedro PB, Almalioglu, Yasin, Markham, Andrew and Trigoni, Niki. Distilling knowledge from a deep pose regressor network. 2019. In Proceedings of Proceedings of the IEEE\/CVF International Conference on Computer Vision, 263-272. https:\/\/doi.org\/10.1109\/iccv.2019.00035"},{"key":"e_1_3_2_1_25_1","volume-title":"Physicochemical Properties of Protein Tertiary Structure Data Set","author":"Rana Prashant Singh","year":"2013","unstructured":"Rana , Prashant Singh . Physicochemical Properties of Protein Tertiary Structure Data Set . March 31, 2013 from https:\/\/archive.ics.uci.edu\/ml\/datasets\/Physicochemical+Properties+of+Protein+Tertiary+Structure Rana, Prashant Singh. Physicochemical Properties of Protein Tertiary Structure Data Set. March 31, 2013 from https:\/\/archive.ics.uci.edu\/ml\/datasets\/Physicochemical+Properties+of+Protein+Tertiary+Structure"},{"key":"e_1_3_2_1_26_1","unstructured":"harlfoxem. House Sales in King County USA. 2016 from https:\/\/www.kaggle.com\/harlfoxem\/housesalesprediction  harlfoxem. House Sales in King County USA. 2016 from https:\/\/www.kaggle.com\/harlfoxem\/housesalesprediction"},{"key":"e_1_3_2_1_27_1","volume-title":"Electrical Grid Stability Simulated Data Data Set","author":"Arzamasov Vadim","year":"2018","unstructured":"Arzamasov , Vadim . Electrical Grid Stability Simulated Data Data Set . November 16, 2018 from https:\/\/archive.ics.uci.edu\/ml\/datasets\/Electrical+Grid+Stability+Simulated+Data+ Arzamasov, Vadim. Electrical Grid Stability Simulated Data Data Set. November 16, 2018 from https:\/\/archive.ics.uci.edu\/ml\/datasets\/Electrical+Grid+Stability+Simulated+Data+"},{"key":"e_1_3_2_1_28_1","unstructured":"Kamath RS and Kamat RK. 2018. Modelling Physicochemical Properties for Protein Tertiary Structure Prediction: Performance Analysis of Regression Models (December 2018).  Kamath RS and Kamat RK. 2018. Modelling Physicochemical Properties for Protein Tertiary Structure Prediction: Performance Analysis of Regression Models (December 2018)."}],"event":{"name":"CSAI 2021: 2021 5th International Conference on Computer Science and Artificial Intelligence","acronym":"CSAI 2021","location":"Beijing China"},"container-title":["2021 5th International Conference on Computer Science and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3507548.3507580","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3507548.3507580","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:26Z","timestamp":1750191146000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3507548.3507580"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,4]]},"references-count":27,"alternative-id":["10.1145\/3507548.3507580","10.1145\/3507548"],"URL":"https:\/\/doi.org\/10.1145\/3507548.3507580","relation":{},"subject":[],"published":{"date-parts":[[2021,12,4]]},"assertion":[{"value":"2022-03-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}