{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T21:30:57Z","timestamp":1781386257856,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":16,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819712762","type":"print"},{"value":"9789819712779","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-1277-9_31","type":"book-chapter","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T02:01:41Z","timestamp":1712023301000},"page":"404-419","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Reducing Overfitting Risk in Small-Sample Learning with ANN: A Case of Predicting Graduate Admission Probability"],"prefix":"10.1007","author":[{"given":"Mengjie","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daomeng","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhilin","family":"Huo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhao","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianghu","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,3]]},"reference":[{"key":"31_CR1","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1992\/3\/032091","volume":"1992","author":"W Zhao","year":"2021","unstructured":"Zhao, W., Wang, Z., Zhu, L.: Research on the application of ai in the field of education big data mining. J. Phys: Conf. Ser. 1992, 032091 (2021). https:\/\/doi.org\/10.1088\/1742-6596\/1992\/3\/032091","journal-title":"J. Phys: Conf. Ser."},{"key":"31_CR2","doi-asserted-by":"publisher","unstructured":"Chiu, T.K.F., Xia, Q., Zhou, X., Chai, C., Cheng, M.: Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education 4 (2023). https:\/\/doi.org\/10.1016\/j.caeai.2022.100118","DOI":"10.1016\/j.caeai.2022.100118"},{"issue":"10","key":"31_CR3","doi-asserted-by":"publisher","first-page":"4049","DOI":"10.1109\/TNNLS.2019.2951803","volume":"31","author":"D Liu","year":"2020","unstructured":"Liu, D., He, Z., Chen, D., Lv, J.: A network framework for small-sample learning. IEEE Trans. Neural Networks Learn. Syst. 31(10), 4049\u20134062 (2020). https:\/\/doi.org\/10.1109\/TNNLS.2019.2951803","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"31_CR4","doi-asserted-by":"publisher","unstructured":"Dong, Y., Li, Y., Zheng, H., Wang, R., Xu, M.: A new dynamic model and transfer learning based intelligent fault diagnosis framework for rolling element bearings race faults: Solving the small sample problem. ISA Trans. 121 (2021). https:\/\/doi.org\/10.1016\/j.isatra.2021.03.042","DOI":"10.1016\/j.isatra.2021.03.042"},{"key":"31_CR5","doi-asserted-by":"publisher","first-page":"6391","DOI":"10.1007\/s10462-021-09975-1","volume":"54","author":"MM Bejani","year":"2021","unstructured":"Bejani, M.M., Ghatee, M.: A systematic review on overfitting control in shallow and deep neural networks. Artif. Intell. Rev. 54, 6391\u20136438 (2021). https:\/\/doi.org\/10.1007\/s10462-021-09975-1","journal-title":"Artif. Intell. Rev."},{"key":"31_CR6","doi-asserted-by":"publisher","first-page":"31114","DOI":"10.1109\/ACCESS.2020.2973304","volume":"8","author":"S Yang","year":"2020","unstructured":"Yang, S., Zhu, X., Zhang, L., Wang, L., Wang, X.: Classification and prediction of Tibetan medical syndrome based on the improved bp neural network. IEEE Access 8, 31114\u201331125 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2973304","journal-title":"IEEE Access"},{"key":"31_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2022.111010","volume":"458","author":"S De","year":"2022","unstructured":"De, S., Doostan, A.: Neural network training using \u21131-regularization and bi-fidelity data. J. Comput. Phys. 458, 111010 (2022). https:\/\/doi.org\/10.1016\/j.jcp.2022.111010","journal-title":"J. Comput. Phys."},{"key":"31_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10845-022-01944-x","volume":"34","author":"Z Xin","year":"2022","unstructured":"Xin, Z., Wang, H., Wu, B., Zhou, Q., Hu, Y.: A novel data-driven method based on sample reliability assessment and improved CNN for machinery fault diagnosis with non-ideal data. J. Intell. Manuf. 34, 1\u201314 (2022). https:\/\/doi.org\/10.1007\/s10845-022-01944-x","journal-title":"J. Intell. Manuf."},{"key":"31_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.apacoust.2023.109209","volume":"203","author":"A Abeysinghe","year":"2023","unstructured":"Abeysinghe, A., Tohmuang, S., Davy, J., Fard, M.: Data augmentation on convolutional neural networks to classify mechanical noise. Appl. Acoust. 203, 109209 (2023). https:\/\/doi.org\/10.1016\/j.apacoust.2023.109209","journal-title":"Appl. Acoust."},{"key":"31_CR10","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0196902","volume":"13","author":"P Kumar","year":"2018","unstructured":"Kumar, P., Belchamber, E., Miklavcic, S.: Pre-processing by data augmentation for improved ellipse fitting. PLoS ONE 13, e0196902 (2018). https:\/\/doi.org\/10.1371\/journal.pone.0196902","journal-title":"PLoS ONE"},{"key":"31_CR11","doi-asserted-by":"publisher","unstructured":"Ha, N.C., Tran, V.-D., Van, L., Than, K.: Eliminating overfitting of probabilistic topic models on short and noisy text: The role of dropout. International J. Approximate Reasoning 112 (2019). https:\/\/doi.org\/10.1016\/j.ijar.2019.05.010","DOI":"10.1016\/j.ijar.2019.05.010"},{"key":"31_CR12","doi-asserted-by":"publisher","DOI":"10.1145\/3624015","author":"J Guo","year":"2023","unstructured":"Guo, J., Qi, L., Shi, Y., Gao, Y.: PLACE dropout: a progressive layer-wise and channel-wise dropout for domain generalization. ACM Trans. Multimed. Comput. Commun. Appl. (2023). https:\/\/doi.org\/10.1145\/3624015","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"issue":"9","key":"31_CR13","doi-asserted-by":"publisher","first-page":"4267","DOI":"10.1109\/TNNLS.2021.3070895","volume":"32","author":"H Li","year":"2021","unstructured":"Li, H., et al.: Adaptive dropout method based on biological principles. IEEE Trans. Neural Networks Learn. Syst. 32(9), 4267\u20134276 (2021). https:\/\/doi.org\/10.1109\/TNNLS.2021.3070895","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"31_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3233\/JIFS-222683","volume":"44","author":"X Li","year":"2023","unstructured":"Li, X., Yu, Q., Yang, Y., Tang, C., Wang, J.: An evolutionary ensemble model based on GA for epidemic transmission prediction. J. Intell. Fuzzy Syst. 44, 1\u201313 (2023). https:\/\/doi.org\/10.3233\/JIFS-222683","journal-title":"J. Intell. Fuzzy Syst."},{"key":"31_CR15","doi-asserted-by":"publisher","unstructured":"Acharya, M.S., Armaan, A., Antony, A.S.: A comparison of regression models for prediction of graduate admissions. In: 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai, India, 2019, pp. 1\u20135 (2019). https:\/\/doi.org\/10.1109\/ICCIDS.2019.8862140","DOI":"10.1109\/ICCIDS.2019.8862140"},{"key":"31_CR16","doi-asserted-by":"publisher","unstructured":"Tang, X., Zheng, D.*, Kebede, G.S., et al.: An automatic segmentation framework of quasi-periodic time series through graph structure. Appl. Intell. (2023). https:\/\/doi.org\/10.1007\/s10489-023-04814-y","DOI":"10.1007\/s10489-023-04814-y"}],"container-title":["Communications in Computer and Information Science","Artificial Intelligence and Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-1277-9_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T10:43:52Z","timestamp":1761216232000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-1277-9_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819712762","9789819712779"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-1277-9_31","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IAIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Artificial Intelligence Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nanjing","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iaic2023a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.iaicconf.com\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}