{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T06:40:26Z","timestamp":1768718426486,"version":"3.49.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T00:00:00Z","timestamp":1591660800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T00:00:00Z","timestamp":1591660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61801501"],"award-info":[{"award-number":["61801501"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61801502"],"award-info":[{"award-number":["61801502"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s10489-020-01717-0","type":"journal-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T06:23:03Z","timestamp":1591683783000},"page":"3429-3440","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Incremental small sphere and large margin for online recognition of communication jamming"],"prefix":"10.1007","volume":"50","author":[{"given":"Yu","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaxing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songhu","family":"Ge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinling","family":"Xing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,6,9]]},"reference":[{"issue":"7","key":"1717_CR1","doi-asserted-by":"publisher","first-page":"1486","DOI":"10.1109\/TIFS.2016.2535906","volume":"11","author":"Q Yan","year":"2016","unstructured":"Yan Q, Zeng H, Jiang T, Li M, Lou W, Hou Y T (2016) Jamming resilient communication using MIMO interference cancellation. IEEE T Inf Foren Sec 11(7):1486\u20131499","journal-title":"IEEE T Inf Foren Sec"},{"key":"1717_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/5457176","volume":"2018","author":"K Ho-Van","year":"2018","unstructured":"Ho-Van K, Do-Dac T (2018) Reliability-Security Trade-Off Analysis of cognitive radio networks with jamming and licensed interference. Wirel Commun Mob Com 2018:1\u201315","journal-title":"Wirel Commun Mob Com"},{"key":"1717_CR3","doi-asserted-by":"crossref","unstructured":"Wu Z, Zhao Y, Yin Z, Luo H (2017) Jamming Signals Classification Using Convolutional Neural Network. In: Proceedings of IEEE International Symposium on Signal Processing and Information Technology. Bilbao, Spain, pp 62\u201367","DOI":"10.1109\/ISSPIT.2017.8388320"},{"key":"1717_CR4","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.neucom.2017.01.103","volume":"268","author":"ME Azami","year":"2017","unstructured":"Azami M E, Lartizien C, Canu S (2017) Converting SVDD scores into probability estimates: Application to outlier detection. Neurocomputing 268:64\u201375","journal-title":"Neurocomputing"},{"issue":"9","key":"1717_CR5","first-page":"1950","volume":"39","author":"G Wang","year":"2017","unstructured":"Wang G, Ren Q, Jiang Z, Liu Y, Xu B (2017) Jamming classification and recognition in transform domain communication system based on signal feature space. J Syst Eng Electron 39(9):1950\u20131958","journal-title":"J Syst Eng Electron"},{"key":"1717_CR6","doi-asserted-by":"crossref","unstructured":"Yue G, Wang X, Madihian M (2007) Design of Anti-Jamming Coding for Cognitive Radio. In: Proceedings of IEEE Global Telecommunications Conference. Washington, pp 4190\u2013 4194","DOI":"10.1109\/GLOCOM.2007.797"},{"issue":"01","key":"1717_CR7","doi-asserted-by":"publisher","first-page":"1958001","DOI":"10.1142\/S0218001419580011","volume":"33","author":"W Huang","year":"2018","unstructured":"Huang W, Liu Z, Lv L, Wang L, Zhang S (2018) A novel Anti-Jamming driven sparse Analysis-Based spread spectrum communication methodology. Int J Pattern Recogn Arti 33(01):1958001","journal-title":"Int J Pattern Recogn Arti"},{"issue":"12","key":"1717_CR8","doi-asserted-by":"publisher","first-page":"5996","DOI":"10.1109\/TWC.2009.12.081627","volume":"8","author":"G Yue","year":"2009","unstructured":"Yue G, Wang X (2009) Anti-jamming coding techniques with application to cognitive radio. IEEE T Wirel Commun 8(12):5996\u20136007","journal-title":"IEEE T Wirel Commun"},{"key":"1717_CR9","unstructured":"Cauwenberghs G, Poggio T (2001) Incremental and decremental support vector machine learning. in proc of advances in neural information processing systems, Vancouver, pp 409\u2013415"},{"key":"1717_CR10","first-page":"1909","volume":"7","author":"P Laskov","year":"2006","unstructured":"Laskov P, Gehl C, Kruger S, Muller K (2006) Incremental support vector learning: analysis, Implementation and Applications. J Mach Learn Res 7:1909\u20131936","journal-title":"J Mach Learn Res"},{"issue":"4","key":"1717_CR11","doi-asserted-by":"publisher","first-page":"e93600","DOI":"10.1371\/journal.pone.0093600","volume":"9","author":"JFG Molina","year":"2014","unstructured":"Molina J F G, Zheng L, Sertdemir M, Dinter D J, Schonberg S, Radle M (2014) Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma. Plos One 9(4):e93600","journal-title":"Plos One"},{"key":"1717_CR12","doi-asserted-by":"crossref","unstructured":"Xie W, Uhlmann S, Kiranyaz S (2014) Incremental learning with support vector data description. In: Proceedings of international conference on pattern recognition, Stockholm, Sweden, pp 3904\u20133909","DOI":"10.1109\/ICPR.2014.669"},{"key":"1717_CR13","doi-asserted-by":"crossref","unstructured":"Tax D M J, Laskov P (2003) Online SVM Learning: from Classification to Data Description and Back. In: Proceedings of IEEE Workshop on Neural Network for Signal Processing. Toulouse, pp 499\u2013508","DOI":"10.1109\/NNSP.2003.1318049"},{"issue":"11","key":"1717_CR14","doi-asserted-by":"publisher","first-page":"2230","DOI":"10.1109\/TSMC.2018.2791511","volume":"49","author":"J Xu","year":"2019","unstructured":"Xu J, Xu C, Zou B, Tang Y Y, Peng J, You X (2019) New incremental learning algorithm with support vector machines. IEEE T Syst Man Cy-S 49(11):2230\u20132241","journal-title":"IEEE T Syst Man Cy-S"},{"key":"1717_CR15","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1016\/j.patcog.2006.06.016","volume":"40","author":"S Cheng","year":"2007","unstructured":"Cheng S, Shih F (2007) An improved incremental training algorithm for support vector machines using active query. Pattern Recogn 40:964\u2013971","journal-title":"Pattern Recogn"},{"key":"1717_CR16","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.patcog.2018.05.023","volume":"83","author":"B Gu","year":"2018","unstructured":"Gu B, Quan X, Gu Y, Sheng V S, Zheng G (2018) Chunk incremental learning for cost-sensitive hinge loss support vector machine. Pattern Recogn 83:196\u2013208","journal-title":"Pattern Recogn"},{"key":"1717_CR17","doi-asserted-by":"publisher","first-page":"1495","DOI":"10.1016\/j.patrec.2006.02.016","volume":"27","author":"S Katagiri","year":"2006","unstructured":"Katagiri S, Abe S (2006) Incremental training of support vector machines using hyperspheres. Pattern Recogn Lett 27:1495\u20131507","journal-title":"Pattern Recogn Lett"},{"issue":"6","key":"1717_CR18","doi-asserted-by":"publisher","first-page":"1158","DOI":"10.1109\/TPAMI.2013.172","volume":"36","author":"R Laxhammar","year":"2014","unstructured":"Laxhammar R, Falkman G (2014) Online learning and sequential anomaly detection in trajectories. IEEE T Pattern Anal 36(6):1158\u20131173","journal-title":"IEEE T Pattern Anal"},{"issue":"4","key":"1717_CR19","doi-asserted-by":"publisher","first-page":"2166","DOI":"10.1109\/TNET.2017.2680245","volume":"25","author":"Y Liu","year":"2017","unstructured":"Liu Y, Liu M (2017) An online learning approach to improving the quality of Crowd-Sourcing. IEEE ACM T Netw 25(4):2166\u20132179","journal-title":"IEEE ACM T Netw"},{"issue":"3","key":"1717_CR20","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1109\/TPAMI.2015.2459678","volume":"38","author":"M Ristin","year":"2016","unstructured":"Ristin M, Guillaumin M, Gall J, Van-Gool L (2016) Incremental learning of random forests for Large-Scale image classification. IEEE T Pattern Anal 38(3):490\u2013503","journal-title":"IEEE T Pattern Anal"},{"issue":"3-4","key":"1717_CR21","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1007\/s00521-013-1534-4","volume":"25","author":"LC Jain","year":"2014","unstructured":"Jain L C, Seera M, Lim C P, Balasubramaniam P (2014) A review of online learning in supervised neural networks. Neural Comput Appl 25(3-4):491\u2013509","journal-title":"Neural Comput Appl"},{"issue":"1","key":"1717_CR22","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1109\/TNNLS.2017.2716952","volume":"29","author":"CLP Chen","year":"2018","unstructured":"Chen C L P, Liu Z (2018) Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE T Neur Net Lear 29(1):10\u201324","journal-title":"IEEE T Neur Net Lear"},{"key":"1717_CR23","doi-asserted-by":"publisher","first-page":"3738","DOI":"10.1016\/j.patcog.2014.06.020","volume":"47","author":"W Deng","year":"2014","unstructured":"Deng W, Hu J, Zhou X, Guo J (2014) Equidistant prototypes embedding for single sample based face recognition with generic learning and incremental learning. Pattern Recogn 47:3738\u20133749","journal-title":"Pattern Recogn"},{"issue":"12","key":"1717_CR24","doi-asserted-by":"publisher","first-page":"3387","DOI":"10.1007\/s00500-014-1492-5","volume":"19","author":"B Krawczyk","year":"2015","unstructured":"Krawczyk B, Wo\u017aniak M (2015) One-class classifiers with incremental learning and forgetting for data streams with concept drift. Soft Comput 19(12):3387\u20133400","journal-title":"Soft Comput"},{"key":"1717_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The nature of statistical learning theory","author":"VN Vapnik","year":"1995","unstructured":"Vapnik V N (1995) The nature of statistical learning theory. Springer, New York"},{"issue":"4","key":"1717_CR26","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1007\/s10489-016-0881-0","volume":"46","author":"S Maldonado","year":"2017","unstructured":"Maldonado S, Lopez J (2017) Robust kernel-based multiclass support vector machines via second-order cone programming. Appl Intell 46(4):983\u2013992","journal-title":"Appl Intell"},{"issue":"11","key":"1717_CR27","doi-asserted-by":"publisher","first-page":"2088","DOI":"10.1109\/TPAMI.2009.24","volume":"31","author":"M Wu","year":"2009","unstructured":"Wu M, Ye J (2009) A small sphere and large margin approach for novelty detection using training data with outliers. IEEE T Pattern Anal 31(11):2088\u20132092","journal-title":"IEEE T Pattern Anal"},{"issue":"2","key":"1717_CR28","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1007\/s10489-016-0836-5","volume":"46","author":"Y Guo","year":"2017","unstructured":"Guo Y, Xiao H, Fu Q (2017) Least square support vector data description for HRRP-based radar target recognition. Appl Intell 46(2):365\u2013372","journal-title":"Appl Intell"},{"issue":"1","key":"1717_CR29","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s10489-009-0176-9","volume":"34","author":"C Li","year":"2011","unstructured":"Li C, Liu K, Wang H (2011) The incremental learning algorithm with support vector machine based on hyperplane-distance. Appl Intell 34(1):19\u201327","journal-title":"Appl Intell"},{"key":"1717_CR30","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","volume":"54","author":"DMJ Tax","year":"2004","unstructured":"Tax D M J, Duin R P W (2004) Support vector data description. Mach Learn 54:45\u201366","journal-title":"Mach Learn"},{"issue":"9","key":"1717_CR31","doi-asserted-by":"publisher","first-page":"2746","DOI":"10.1007\/s10489-017-1111-0","volume":"48","author":"Y Guo","year":"2018","unstructured":"Guo Y, Xiao H (2018) Multiclass multiple kernel learning using hypersphere for pattern recognition. Appl Intell 48(9):2746\u20132754","journal-title":"Appl Intell"},{"issue":"3","key":"1717_CR32","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1109\/LWC.2019.2900247","volume":"8","author":"Y Zeng","year":"2019","unstructured":"Zeng Y, Zhang M, Han F, Gong Y, Zhang J (2019) Spectrum analysis and convolutional neural network for automatic modulation recognition. IEEE Wirel Commun Le 8(3):929\u2013932","journal-title":"IEEE Wirel Commun Le"},{"issue":"2","key":"1717_CR33","first-page":"1:50","volume":"49","author":"P Branco","year":"2015","unstructured":"Branco P, Torgo L, Ribeiro R (2015) A survey of predictive modelling under imbalanced distributions. ACM Comput Surv 49(2):1:50","journal-title":"ACM Comput Surv"},{"key":"1717_CR34","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1016\/j.ins.2013.04.016","volume":"257","author":"A Maratea","year":"2014","unstructured":"Maratea A, Petrosino A, Manzo M (2014) Adjusted F-measure and Kernel Scaling for imbalanced Data Learning. Inform Sci 257:331\u2013341","journal-title":"Inform Sci"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01717-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-020-01717-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-020-01717-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,8]],"date-time":"2021-06-08T23:56:37Z","timestamp":1623196597000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-020-01717-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,9]]},"references-count":34,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["1717"],"URL":"https:\/\/doi.org\/10.1007\/s10489-020-01717-0","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,9]]},"assertion":[{"value":"9 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}