{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:26:07Z","timestamp":1740122767902,"version":"3.37.3"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,6,8]],"date-time":"2021-06-08T00:00:00Z","timestamp":1623110400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,8]],"date-time":"2021-06-08T00:00:00Z","timestamp":1623110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Speech Technol"],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1007\/s10772-021-09861-9","type":"journal-article","created":{"date-parts":[[2021,6,8]],"date-time":"2021-06-08T17:03:17Z","timestamp":1623171797000},"page":"1007-1015","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Modified self-training based statistical models for image classification and speaker identification"],"prefix":"10.1007","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5185-882X","authenticated-orcid":false,"given":"Jyostna Devi","family":"Bodapati","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,8]]},"reference":[{"key":"9861_CR14","unstructured":"Blum, A., & Chawla, S. (2001). Learning from labeled and unlabeled data using graph mincuts. In Proceedings of the eighteenth international conference on machine learning (pp. 19\u201326)."},{"key":"9861_CR15","doi-asserted-by":"crossref","unstructured":"Blum, A., Lafferty, J., Rwebangira, M. R., & Reddy, R. (2004). Semi-supervised learning using randomized mincuts. In Proceedings of the twenty-first international conference on Machine learning (p. 13). ACM.","DOI":"10.1145\/1015330.1015429"},{"key":"9861_CR3","doi-asserted-by":"crossref","unstructured":"Bodapati, J. D., & Veeranjaneyulu, N. (2017). Abnormal network traffic detection using support vector data description. In Proceedings of the 5th international conference on frontiers in intelligent computing: Theory and applications (pp. 497\u2013506). Springer.","DOI":"10.1007\/978-981-10-3153-3_49"},{"issue":"1","key":"9861_CR5","doi-asserted-by":"publisher","first-page":"125","DOI":"10.18280\/isi.240119","volume":"24","author":"JD Bodapat","year":"2019","unstructured":"Bodapat, J. D., Veeranjaneyulu, N., & Shareef Shaik (2019). Sentiment analysis from movie reviews using LSTMs. Ing\u00e9nierie des Syst\u00e8mes d Inf, 24(1), 125\u2013129.","journal-title":"Ing\u00e9nierie des Syst\u00e8mes d Inf"},{"key":"9861_CR4","doi-asserted-by":"publisher","first-page":"259","DOI":"10.18280\/isi.250214","volume":"25","author":"JD Bodapati","year":"2020","unstructured":"Bodapati, J. D., Vijay, A., & Veeranjaneyulu, N. (2020). Brain tumor detection using deep features in the latent space. Ing\u00e9nierie des Syst\u00e8mes d\u2019Information, 25, 259\u2013265.","journal-title":"Ing\u00e9nierie des Syst\u00e8mes d\u2019Information"},{"issue":"3","key":"9861_CR11","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1109\/TNN.2009.2015974","volume":"20","author":"O Chapelle","year":"2009","unstructured":"Chapelle, O., Scholkopf, B., & Zien, A. (2009). Semi-supervised learning (Chapelle, O. et al., eds.; 2006) [book reviews]. IEEE Transactions on Neural Networks, 20(3), 542\u2013542.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"9861_CR1","doi-asserted-by":"crossref","unstructured":"\u010cular, L., Tomai\u0107, M., Suba\u0161i\u0107, M., \u0160ari\u0107, T., Sajkovi\u0107, V., & Vodanovi\u0107, M. (2017). Dental age estimation from panoramic X-ray images using statistical models. In Proceedings of the 10th international symposium on image and signal processing and analysis (pp. 25\u201330). IEEE.","DOI":"10.1109\/ISPA.2017.8073563"},{"issue":"6","key":"9861_CR18","doi-asserted-by":"publisher","first-page":"942","DOI":"10.1109\/LGRS.2018.2817361","volume":"15","author":"A Davari","year":"2018","unstructured":"Davari, A., Aptoula, E., Yanikoglu, B., Maier, A., & Riess, C. (2018). GMM-based synthetic samples for classification of hyperspectral images with limited training data. IEEE Geoscience and Remote Sensing Letters, 15(6), 942\u2013946.","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"issue":"1","key":"9861_CR26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (methodological), 39(1), 1\u201338.","journal-title":"Journal of the Royal Statistical Society: Series B (methodological)"},{"key":"9861_CR23","doi-asserted-by":"crossref","unstructured":"Duan, R., Jiang, W., & Man, H. (2006). Semi-supervised image classification in likelihood space. In 2006 IEEE international conference on image processing (pp. 957\u2013960). IEEE.","DOI":"10.1109\/ICIP.2006.312634"},{"issue":"5","key":"9861_CR10","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1016\/j.jbi.2013.06.014","volume":"46","author":"V Garla","year":"2013","unstructured":"Garla, V., Taylor, C., & Brandt, C. (2013). Semi-supervised clinical text classification with Laplacian SVMs: An application to cancer case management. Journal of Biomedical Informatics, 46(5), 869\u2013875.","journal-title":"Journal of Biomedical Informatics"},{"key":"9861_CR16","unstructured":"Jaakkola, T., & Szummer, M. (2002). Partially labeled classification with Markov random walks. In Advances in neural information processing systems (pp. 945\u2013952)."},{"key":"9861_CR13","unstructured":"Joachims, T. (2003). Transductive learning via spectral graph partitioning. In International conference on machine learning (pp. 290\u2013297)."},{"key":"9861_CR9","unstructured":"Kveton, B., Valko, M., Rahimi, A., & Huang, L. (2010). Semi-supervised learning with max-margin graph cuts. In International conference on artificial intelligence and statistics (pp. 421\u2013428)."},{"key":"9861_CR25","doi-asserted-by":"crossref","unstructured":"Li, Y.-F., & Zhou, Z.-H. (2011). Improving semi-supervised support vector machines through unlabeled instances selection. In Proceedings of the twenty-fifth AAAI conference on artificial intelligence (pp. 386\u2013391).","DOI":"10.1609\/aaai.v25i1.7920"},{"key":"9861_CR19","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1016\/j.procs.2017.12.112","volume":"125","author":"A Maurya","year":"2018","unstructured":"Maurya, A., Kumar, D., & Agarwal, R. K. (2018). Speaker recognition for Hindi speech signal using MFCC-GMM approach. Procedia Computer Science, 125, 880\u2013887.","journal-title":"Procedia Computer Science"},{"issue":"11","key":"9861_CR22","doi-asserted-by":"publisher","first-page":"1468","DOI":"10.1109\/TPAMI.2003.1240120","volume":"25","author":"DJ Miller","year":"2003","unstructured":"Miller, D. J. (2003). A mixture model and EM-based algorithm for class discovery, robust classification, and outlier rejection in mixed labeled\/unlabeled data sets. Pattern Analysis and Machine Intelligence, 25(11), 1468\u20131483.","journal-title":"Pattern Analysis and Machine Intelligence"},{"issue":"3","key":"9861_CR28","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1023\/A:1011139631724","volume":"42","author":"A Oliva","year":"2001","unstructured":"Oliva, A., & Torralba, A. (2001). Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision, 42(3), 145\u2013175.","journal-title":"International Journal of Computer Vision"},{"key":"9861_CR20","unstructured":"Patel, P., Chaudhari, A., Kale, R., & Pund, M. (2017). Emotion recognition from speech with Gaussian mixture models & via boosted GMM. International Journal of Research in Science and Engineering, 3."},{"issue":"2","key":"9861_CR21","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1162\/COLI_a_00286","volume":"43","author":"H Sajjad","year":"2017","unstructured":"Sajjad, H., Schmid, H., Fraser, A., & Sch\u00fctze, H. (2017). Statistical models for unsupervised, semi-supervised, and supervised transliteration mining. Computational Linguistics, 43(2), 349\u2013375.","journal-title":"Computational Linguistics"},{"issue":"5","key":"9861_CR8","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1109\/36.312897","volume":"32","author":"BM Shahshahani","year":"1994","unstructured":"Shahshahani, B. M. (1994). The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon. Geoscience and Remote Sensing, 32(5), 1087\u20131095.","journal-title":"Geoscience and Remote Sensing"},{"key":"9861_CR17","doi-asserted-by":"crossref","unstructured":"Sindhwani, V., Niyogi, P., & Belkin, M. (2005). Beyond the point cloud: from transductive to semi-supervised learning. In Proceedings of the 22nd international conference on Machine learning (pp. 824\u2013831). ACM.","DOI":"10.1145\/1102351.1102455"},{"issue":"1","key":"9861_CR7","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/s13042-015-0328-7","volume":"8","author":"J Tanha","year":"2017","unstructured":"Tanha, J., van Someren, M., & Afsarmanesh, H. (2017). Semi-supervised self-training for decision tree classifiers. International Journal of Machine Learning and Cybernetics, 8(1), 355\u2013370.","journal-title":"International Journal of Machine Learning and Cybernetics"},{"key":"9861_CR24","doi-asserted-by":"crossref","unstructured":"Vatsavai, R. R., Badhuri, B., Shekhar, S., & Burk, T. E. (2008). Multisource data classification using a hybrid semi-supervised learning scheme. In IEEE international geoscience and remote sensing symposium, 2008. IGARSS 2008 (Vol. 3, pp. III-1016). IEEE.","DOI":"10.1109\/IGARSS.2008.4779525"},{"key":"9861_CR12","doi-asserted-by":"publisher","first-page":"391","DOI":"10.18280\/isi.250315","volume":"25","author":"N Veeranjaneyulu","year":"2020","unstructured":"Veeranjaneyulu, N., Bodapati, J. D., & Buradagunta, S. (2020). Classifying limited resource data using semi-supervised SVM classifying limited resource data using semi-supervised SVM. Ing\u00e9nierie des Syst\u00e8mes d\u2019Information, 25, 391\u2013395.","journal-title":"Ing\u00e9nierie des Syst\u00e8mes d\u2019Information"},{"issue":"5","key":"9861_CR6","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1109\/TNNLS.2012.2186825","volume":"23","author":"Y Wang","year":"2012","unstructured":"Wang, Y., Chen, S., & Zhou, Z.-H. (2012). New semi-supervised classification method based on modified cluster assumption. IEEE Transactions on Neural Networks and Learning Systems, 23(5), 689\u2013702.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"4","key":"9861_CR2","first-page":"361","volume":"7","author":"J Woo","year":"2019","unstructured":"Woo, J., Xing, F., Stone, M., Green, J., Reese, T. G., Brady, T. J., Prince, J. L., El Fakhri, G. (2019). Speech map: A statistical multimodal atlas of 4D tongue motion during speech from tagged and cine MR images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 7(4), 361\u2013373.","journal-title":"Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization"}],"container-title":["International Journal of Speech Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10772-021-09861-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10772-021-09861-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10772-021-09861-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,1]],"date-time":"2024-09-01T12:24:43Z","timestamp":1725193483000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10772-021-09861-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,8]]},"references-count":27,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["9861"],"URL":"https:\/\/doi.org\/10.1007\/s10772-021-09861-9","relation":{},"ISSN":["1381-2416","1572-8110"],"issn-type":[{"type":"print","value":"1381-2416"},{"type":"electronic","value":"1572-8110"}],"subject":[],"published":{"date-parts":[[2021,6,8]]},"assertion":[{"value":"18 March 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}