{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T10:26:29Z","timestamp":1743157589860,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819947515"},{"type":"electronic","value":"9789819947522"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-981-99-4752-2_15","type":"book-chapter","created":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T16:02:10Z","timestamp":1690732930000},"page":"179-190","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["K-means Based Transfer Learning Algorithm"],"prefix":"10.1007","author":[{"given":"Yuanyuan","family":"Du","sequence":"first","affiliation":[]},{"given":"Bo","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zhonghua","family":"Quan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,31]]},"reference":[{"issue":"1","key":"15_CR1","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s00287-016-1013-2","volume":"40","author":"C Wick","year":"2016","unstructured":"Wick, C.: Deep Learning. Informatik-Spektrum 40(1), 103\u2013107 (2016). https:\/\/doi.org\/10.1007\/s00287-016-1013-2","journal-title":"Informatik-Spektrum"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"Gopalan, R., Li, R., Chellappa, R.: Domain adaptation for object recognition: an unsupervised approach. In: 2011 International Conference on Computer Vision, pp. 999\u20131006. IEEEComputer Society, Barcelona (2011)","DOI":"10.1109\/ICCV.2011.6126344"},{"key":"15_CR3","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"15_CR4","doi-asserted-by":"publisher","first-page":"3759","DOI":"10.1109\/TNNLS.2019.2899037","volume":"30","author":"L Zhang","year":"2019","unstructured":"Zhang, L., Wang, S., Huang, G., Zuo, W., Yang, J., Zhang, D.: Manifold criterion guided transfer learning via intermediate domain generation. IEEE Trans. Neural Netw. Learn. Syst. 30, 3759\u20133773 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"15_CR5","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2019","unstructured":"Zhuang, F., et al.: A comprehensive survey on transfer learning. Proc. IEEE 109, 43\u201376 (2019)","journal-title":"Proc. IEEE"},{"key":"15_CR6","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1109\/TPAMI.2018.2832198","volume":"41","author":"J Liang","year":"2019","unstructured":"Liang, J., He, R., Sun, Z., Tan, T.: Aggregating randomized clustering-promoting invariant projections for domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell. 41, 1027\u20131042 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, Y., Hao, S., Feng, W., Shen, Z.: Balanced distribution adaptation for transfer learning. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 1129\u20131134. IEEE Computer Society, New Orleans (2017)","DOI":"10.1109\/ICDM.2017.150"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, Y., Yu, H., Huang, M., Yang, Q.: Easy transfer learning by exploiting intra-domain structures. In: 2019 IEEE International Conference on Multimedia and Expo (ICME), pp. 1210\u20131215. IEEE, Shanghai (2019)","DOI":"10.1109\/ICME.2019.00211"},{"key":"15_CR9","doi-asserted-by":"publisher","first-page":"4842","DOI":"10.1109\/TNNLS.2019.2958152","volume":"31","author":"S Li","year":"2020","unstructured":"Li, S., et al.: Discriminative transfer feature and label consistency for cross-domain image classification. IEEE Trans. Neural Netw. Learn. Syst. 31, 4842\u20134856 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"2","key":"15_CR10","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/s40745-015-0040-1","volume":"2","author":"D Xu","year":"2015","unstructured":"Xu, D., Tian, Y.: A comprehensive survey of clustering algorithms. Ann. Data Sci. 2(2), 165\u2013193 (2015). https:\/\/doi.org\/10.1007\/s40745-015-0040-1","journal-title":"Ann. Data Sci."},{"key":"15_CR11","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1109\/TNN.2010.2091281","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan, S.J., Tsang, I.W., Kwok, J.T., Yang, Q.: Domain adaptation via transfer component analysis. IEEE Trans. Neural Netw. 22, 199\u2013210 (2009)","journal-title":"IEEE Trans. Neural Netw."},{"key":"15_CR12","doi-asserted-by":"publisher","first-page":"e49","DOI":"10.1093\/bioinformatics\/btl242","volume":"22","author":"KM Borgwardt","year":"2006","unstructured":"Borgwardt, K.M., Gretton, A., Rasch, M.J., Kriegel, H., Sch\u00f6lkopf, B., Smola, A.: Integrating structured biological data by Kernel maximum mean discrepancy. Bioinformatics 22, e49-57 (2006)","journal-title":"Bioinformatics"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: Transfer feature learning with joint distribution adaptation. In: 2013 IEEE International Conference on Computer Vision, pp. 2200\u20132207. IEEE Computer Society, Sydney (2013)","DOI":"10.1109\/ICCV.2013.274"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2066\u20132073. IEEE Computer Society, Providence (2012)","DOI":"10.1109\/CVPR.2012.6247911"},{"key":"15_CR15","doi-asserted-by":"publisher","first-page":"3374","DOI":"10.1109\/TNNLS.2019.2944455","volume":"31","author":"L Zhang","year":"2020","unstructured":"Zhang, L., Fu, J., Wang, S., Zhang, D., Dong, Z.Y., Philip Chen, C.L.: Guide subspace learning for unsupervised domain adaptation. IEEE Trans. Neural Netw. Learn. Syst. 31, 3374\u20133388 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"15_CR16","doi-asserted-by":"crossref","unstructured":"Tang, H., Chen, K., Jia, K.: Unsupervised domain adaptation via structurally regularized deep clustering. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8722\u20138732. Computer Vision Foundation\/IEEE, Seattle (2020)","DOI":"10.1109\/CVPR42600.2020.00875"},{"key":"15_CR17","doi-asserted-by":"publisher","first-page":"5481","DOI":"10.1109\/TCSVT.2022.3151387","volume":"32","author":"M Meng","year":"2022","unstructured":"Meng, M., Wu, Z., Liang, T., Yu, J., Wu, J.: Exploring fine-grained cluster structure knowledge for unsupervised domain adaptation. IEEE Trans. Circuits Syst. Video Technol. 32, 5481\u20135494 (2022)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Chaudhuri, D., Chaudhuri, B.B.: A novel multiseed nonhierarchical data clustering technique. In: IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics: A Publication of the IEEE Systems, Man, and Cybernetics Society. vol. 27, pp. 871\u2013876 (1997)","DOI":"10.1109\/3477.623240"},{"key":"15_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1007\/978-3-319-11382-1_18","volume-title":"Information Access Evaluation. Multilinguality, Multimodality, and Interaction","author":"B Caputo","year":"2014","unstructured":"Caputo, B., et al.: ImageCLEF 2014: overview and analysis of the results. In: Kanoulas, E., et al. (eds.) CLEF 2014. LNCS, vol. 8685, pp. 192\u2013211. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-11382-1_18"},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Fang, C., Xu, Y., Rockmore, D.N. Unbiased metric learning: on the utilization of multiple datasets and web images for softening bias. In: 2013 IEEE International Conference on Computer Vision, pp. 1657\u20131664. IEEE Computer Society, Sydney (2013)","DOI":"10.1109\/ICCV.2013.208"},{"key":"15_CR21","unstructured":"Donahue, J., et al.: DeCAF: a deep convolutional activation feature for generic visual recognition. In: Proceedings of the 31th International Conference on Machine Learning (ICML), pp. 647\u2013655. JMLR.org, Beijing (2013)"},{"key":"15_CR22","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84\u201390 (2012)","journal-title":"Commun. ACM"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE Computer Society, Miami (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778. IEEE Computer Society, Las Vegas (2015)","DOI":"10.1109\/CVPR.2016.90"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Du, Y., et al.: Classifier Adaptation Based on Modified Label Propagation for Unsupervised Domain Adaptation. Wirel. Commun. Mob. Comput. 2022, 2963195 (2022)","DOI":"10.1155\/2022\/2963195"},{"key":"15_CR26","unstructured":"Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: ACM-SIAM Symposium on Discrete Algorithms, pp. 1027--1035. SIAM, New Orleans (2007)"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-4752-2_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T09:20:26Z","timestamp":1729848026000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-4752-2_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819947515","9789819947522"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-4752-2_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"31 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhengzhou","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":"10 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2023a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2023\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}