{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T07:59:41Z","timestamp":1742975981201,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030005627"},{"type":"electronic","value":"9783030005634"}],"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\/978-3-030-00563-4_30","type":"book-chapter","created":{"date-parts":[[2018,10,5]],"date-time":"2018-10-05T11:35:29Z","timestamp":1538739329000},"page":"315-324","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Novel Semi-supervised Classification Method Based on Class Certainty of Samples"],"prefix":"10.1007","author":[{"given":"Fei","family":"Gao","sequence":"first","affiliation":[]},{"given":"Zhenyu","family":"Yue","sequence":"additional","affiliation":[]},{"given":"Qingxu","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Erfu","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Amir","family":"Hussain","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,10,6]]},"reference":[{"issue":"8","key":"30_CR1","doi-asserted-by":"publisher","first-page":"4418","DOI":"10.1109\/TGRS.2015.2398468","volume":"53","author":"J Zabalza","year":"2015","unstructured":"Zabalza, J., Ren, J., Zheng, J., Han, J.: Novel two-dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging. IEEE Trans. Geosci. Remote Sens. 53(8), 4418\u20134433 (2015)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"24","key":"30_CR2","doi-asserted-by":"publisher","first-page":"8669","DOI":"10.1080\/01431161.2013.845924","volume":"34","author":"C Zhao","year":"2013","unstructured":"Zhao, C., Li, X., Ren, J., Marshall, S.: Improved sparse representation using adaptive spatial support for effective target detection in hyperspectral imagery. Int. J. Remote Sens. 34(24), 8669\u20138684 (2013)","journal-title":"Int. J. Remote Sens."},{"issue":"6","key":"30_CR3","doi-asserted-by":"publisher","first-page":"3325","DOI":"10.1109\/TGRS.2014.2374218","volume":"53","author":"J Han","year":"2015","unstructured":"Han, J.: Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning. IEEE Trans. Geosci. Remote Sens. 53(6), 3325\u20133337 (2015)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"30_CR4","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.patcog.2018.02.004","volume":"79","author":"Y Yan","year":"2018","unstructured":"Yan, Y.: Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement. Pattern Recogn. 79, 65\u201378 (2018)","journal-title":"Pattern Recogn."},{"key":"30_CR5","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.neucom.2018.01.076","volume":"287","author":"Z Wang","year":"2018","unstructured":"Wang, Z.: A deep-learning based feature hybrid framework for spatiotemporal saliency detection inside videos. Neurocomputing 287, 68\u201383 (2018)","journal-title":"Neurocomputing"},{"issue":"1","key":"30_CR6","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s12559-012-9147-2","volume":"5","author":"X Bian","year":"2013","unstructured":"Bian, X., Zhang, T., Zhang, X.: Clustering-based extraction of near border data samples for remote sensing image classification. Cogn. Comput. 5(1), 19\u201331 (2013)","journal-title":"Cogn. Comput."},{"key":"30_CR7","first-page":"1","volume":"99","author":"F Cao","year":"2018","unstructured":"Cao, F.: Sparse representation-based augmented multinomial logistic extreme learning machine with weighted composite features for spectral-spatial classification of hyperspectral images. IEEE Trans. Geosci. Remote Sens. 99, 1\u201317 (2018)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"4","key":"30_CR8","doi-asserted-by":"publisher","first-page":"2217","DOI":"10.1109\/TGRS.2013.2258676","volume":"52","author":"E Pasolli","year":"2014","unstructured":"Pasolli, E., Melgani, F., Tuia, D., Pacifici, F., Emery, W.J.: SVM active learning approach for image classification using spatial information. IEEE Trans. Geosci. Remote Sens. 52(4), 2217\u20132233 (2014)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"unstructured":"Blum, A., Chawla, S.: Learning from labeled and unlabeled data using graph mincuts. In: Eighteenth International Conference on Machine Learning, pp. 19\u201326. Morgan Kaufmann Publishers, USA (2001)","key":"30_CR9"},{"unstructured":"Blum, A.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 92\u2013100 (2000)","key":"30_CR10"},{"unstructured":"Joachims, T.: Transductive inference for text classification using support vector machines. In: Sixteenth International Conference on Machine Learning, pp. 200\u2013209. Morgan Kaufmann Publishers, Slovenia (1999)","key":"30_CR11"},{"issue":"11","key":"30_CR12","doi-asserted-by":"publisher","first-page":"6937","DOI":"10.1109\/TGRS.2014.2305805","volume":"52","author":"C Persello","year":"2014","unstructured":"Persello, C., Bruzzone, L.: Active and semisupervised learning for the classification of remote sensing images. IEEE Trans. Geosci. Remote Sens. 52(11), 6937\u20136956 (2014)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"11","key":"30_CR13","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1109\/TKDE.2005.186","volume":"17","author":"Z Zhi-Hua","year":"2005","unstructured":"Zhi-Hua, Z., Ming, L.: Tri-training: exploiting unlabeled data using three classifiers. IEEE Trans. Knowl. Data Eng. 17(11), 1529\u20131541 (2005)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"30_CR14","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/TKDE.2007.190672","volume":"20","author":"F Wang","year":"2007","unstructured":"Wang, F., Zhang, C.: Label propagation through linear neighborhoods. IEEE Trans. Knowl. Data Eng. 20(1), 55\u201367 (2007)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"unstructured":"Wagstaff, K., Cardie, C., Rogers, S.: Constrained K-means clustering with background knowledge. In: Eighteenth International Conference on Machine Learning, pp. 577\u2013584. Morgan Kaufmann Publishers, USA (2001)","key":"30_CR15"},{"doi-asserted-by":"crossref","unstructured":"Cai, D., He, X., Han, J.: Semi-supervised discriminant analysis. In: 11th International Conference on Computer Vision, pp. 1\u20137. IEEE, Brazil (2007)","key":"30_CR16","DOI":"10.1109\/ICCV.2007.4408856"}],"container-title":["Lecture Notes in Computer Science","Advances in Brain Inspired Cognitive Systems"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-00563-4_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,11,16]],"date-time":"2018-11-16T18:26:30Z","timestamp":1542392790000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-00563-4_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030005627","9783030005634"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-00563-4_30","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":"BICS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Brain Inspired Cognitive Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","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":"7 July 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 July 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bics2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/bics2018.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}