{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:21:26Z","timestamp":1740108086342,"version":"3.37.3"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2017,10,26]],"date-time":"2017-10-26T00:00:00Z","timestamp":1508976000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100003398","name":"Shanxi Scholarship Council of China","doi-asserted-by":"crossref","award":["201406685066"],"award-info":[{"award-number":["201406685066"]}],"id":[{"id":"10.13039\/501100003398","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2019,7]]},"DOI":"10.1007\/s00521-017-3257-4","type":"journal-article","created":{"date-parts":[[2017,10,26]],"date-time":"2017-10-26T11:33:52Z","timestamp":1509017632000},"page":"3107-3116","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Query-specific optimal convolutional neural ranker"],"prefix":"10.1007","volume":"31","author":[{"given":"Jingzheng","family":"Yao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2648-9986","authenticated-orcid":false,"given":"Feng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanyan","family":"Geng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,10,26]]},"reference":[{"key":"3257_CR1","doi-asserted-by":"crossref","unstructured":"Akiyama H, Tsuji M, Aramaki S (2016) Learning evaluation function for decision making of soccer agents using learning to rank. In: Proceedings\u20142016 joint 8th international conference on soft computing and intelligent systems and 2016 17th international symposium on advanced intelligent systems, SCIS\u2013ISIS 2016, pp 239\u2013242","DOI":"10.1109\/SCIS-ISIS.2016.0059"},{"key":"3257_CR2","doi-asserted-by":"crossref","unstructured":"Burges C, Shaked T, Renshaw E, Lazier A, Deeds M, Hamilton N, Hullender G (2005) Learning to rank using gradient descent. In: Proceedings of the 22nd international conference on Machine learning. ACM, pp 89\u201396","DOI":"10.1145\/1102351.1102363"},{"key":"3257_CR3","doi-asserted-by":"crossref","unstructured":"Cao Z, Qin T, Liu TY, Tsai MF, Li H (2007) Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the 24th international conference on Machine learning. ACM, pp 129\u2013136","DOI":"10.1145\/1273496.1273513"},{"key":"3257_CR4","unstructured":"Chen Y, Xie W, Gunter CA, Liebovitz D, Mehrotra S, Zhang H, Malin B (2015) Inferring clinical workflow efficiency via electronic medical record utilization. In: AMIA annual symposium proceedings, vol 2015. American Medical Informatics Association, p 416"},{"key":"3257_CR5","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition, CVPR 2009, IEEE, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"3257_CR6","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.patcog.2016.11.015","volume":"64","author":"Y Duan","year":"2017","unstructured":"Duan Y, Liu F, Jiao L, Zhao P, Zhang L (2017) Sar image segmentation based on convolutional-wavelet neural network and markov random field. Pattern Recognit 64:255\u2013267","journal-title":"Pattern Recognit"},{"key":"3257_CR7","doi-asserted-by":"publisher","unstructured":"Fan J, Liang RZ (2016) Stochastic learning of multi-instance dictionary for earth movers distance-based histogram comparison. Neural Comput Appl. doi:\n                    10.1007\/s00521-016-2603-2","DOI":"10.1007\/s00521-016-2603-2"},{"issue":"4","key":"3257_CR8","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1109\/TPAMI.2006.79","volume":"28","author":"L Fei-Fei","year":"2006","unstructured":"Fei-Fei L, Fergus R, Perona P (2006) One-shot learning of object categories. IEEE Trans Pattern Anal Mach Intell 28(4):594\u2013611","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3257_CR9","unstructured":"Geng Y, Liang RZ, Li W, Wang J, Liang G, Xu C, Wang JY (2016) Learning convolutional neural network to maximize pos@ top performance measure. arXiv preprint \n                    arXiv:1609.08417"},{"key":"3257_CR10","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-3-319-49421-0_4","volume":"269","author":"R Haas","year":"2017","unstructured":"Haas R, Hummel B (2017) Learning to rank extract method refactoring suggestions for long methods. Lecture Notes Bus Inf Process 269:45\u201356","journal-title":"Lecture Notes Bus Inf Process"},{"key":"3257_CR11","doi-asserted-by":"crossref","unstructured":"Li L, Yao Y, Tang J, Fan W, Tong H (2016) Quint: on query-specific optimal networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 985\u2013994","DOI":"10.1145\/2939672.2939768"},{"key":"3257_CR12","doi-asserted-by":"publisher","unstructured":"Li Q, Zhou X, Gu A, Li Z, Liang RZ (2016) Nuclear norm regularized convolutional max pos@top machine. Neural Comput Appl. doi:\n                    10.1007\/s00521-016-2680-2","DOI":"10.1007\/s00521-016-2680-2"},{"key":"3257_CR13","unstructured":"Liang RZ, Shi L, Wang H, Meng J, Wang JJY, Sun Q, Gu Y (2016) Optimizing top precision performance measure of content-based image retrieval by learning similarity function. In: 2016 23st International conference on pattern recognition (ICPR). IEEE"},{"key":"3257_CR14","doi-asserted-by":"crossref","unstructured":"Liang RZ, Xie W, Li W, Wang H, Wang JJY, Taylor L (2016) A novel transfer learning method based on common space mapping and weighted domain matching. In: 2016 IEEE 28th international conference on tools with artificial intelligence (ICTAI). IEEE, pp 299\u2013303","DOI":"10.1109\/ICTAI.2016.0053"},{"key":"3257_CR15","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/j.inffus.2016.12.001","volume":"36","author":"Y Liu","year":"2017","unstructured":"Liu Y, Chen X, Peng H, Wang Z (2017) Multi-focus image fusion with a deep convolutional neural network. Inf Fusion 36:191\u2013207","journal-title":"Inf Fusion"},{"key":"3257_CR16","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.jbi.2017.01.002","volume":"66","author":"L Ma","year":"2017","unstructured":"Ma L, Liu X, Gao Y, Zhao Y, Zhao X, Zhou C (2017) A new method of content based medical image retrieval and its applications to ct imaging sign retrieval. J Biomed Inform 66:148\u2013158","journal-title":"J Biomed Inform"},{"key":"3257_CR17","unstructured":"Mao H, Liu H, Shi P (2008) Neighbor-constrained active contour without edges. In: IEEE computer society conference on computer vision and pattern recognition workshops, 2008. CVPRW\u201908, IEEE, pp 1\u20137"},{"key":"3257_CR18","doi-asserted-by":"crossref","unstructured":"Mao H, Liu H, Shi P (2010) A convex neighbor-constrained active contour model for image segmentation. In: 2010 17th IEEE international conference on image processing (ICIP), IEEE, pp 793\u2013796","DOI":"10.1109\/ICIP.2010.5652625"},{"key":"3257_CR19","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.inffus.2017.01.003","volume":"37","author":"L Piras","year":"2017","unstructured":"Piras L, Giacinto G (2017) Information fusion in content based image retrieval: a comprehensive overview. Inf Fusion 37:50\u201360","journal-title":"Inf Fusion"},{"key":"3257_CR20","doi-asserted-by":"publisher","unstructured":"Ren X, Chen K, Yang X, Zhou Y, He J, Sun J (2017) A novel scene text detection algorithm based on convolutional neural network. In: VCIP 2016\u201430th anniversary of visual communication and image processing, p 7805444. doi:\n                    10.1109\/VCIP.2016.7805444","DOI":"10.1109\/VCIP.2016.7805444"},{"key":"3257_CR21","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1016\/j.patcog.2016.09.032","volume":"63","author":"B Shi","year":"2017","unstructured":"Shi B, Chen Y, Zhang P, Smith CD, Liu J, Initiative ADN et al (2017) Nonlinear feature transformation and deep fusion for alzheimer\u2019s disease staging analysis. Pattern Recognit 63:487\u2013498","journal-title":"Pattern Recognit"},{"key":"3257_CR22","doi-asserted-by":"crossref","unstructured":"Tian Q, Li B (2016) Weakly hierarchical lasso based learning to rank in best answer prediction. In: Proceedings of the 2016 IEEE\/ACM international conference on advances in social networks analysis and mining, ASONAM 2016, pp 307\u2013314","DOI":"10.1109\/ASONAM.2016.7752250"},{"key":"3257_CR23","doi-asserted-by":"crossref","unstructured":"Wang H, Wang J (2014) An effective image representation method using kernel classification. In: 2014 IEEE 26th international conference on tools with artificial intelligence (ICTAI 2014), pp 853\u2013858","DOI":"10.1109\/ICTAI.2014.131"},{"key":"3257_CR24","doi-asserted-by":"publisher","unstructured":"Wu Y, Wang L, Cui F, Zhai H, Dong B, Wang JY (2017) Cross-model convolutional neural network for multiple modality data representation. Neural Comput Appl. doi:\n                    10.1007\/s00521-016-2824-4","DOI":"10.1007\/s00521-016-2824-4"},{"key":"3257_CR25","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/j.ins.2016.12.030","volume":"387","author":"Z Xia","year":"2017","unstructured":"Xia Z, Xiong N, Vasilakos A, Sun X (2017) Epcbir: an efficient and privacy-preserving content-based image retrieval scheme in cloud computing. Inf Sci 387:195\u2013204","journal-title":"Inf Sci"},{"key":"3257_CR26","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.neunet.2016.12.008","volume":"88","author":"J Xu","year":"2017","unstructured":"Xu J, Xu B, Wang P, Zheng S, Tian G, Zhao J, Xu B (2017) Self-taught convolutional neural networks for short text clustering. Neural Netw 88:22\u201331","journal-title":"Neural Netw"},{"issue":"4","key":"3257_CR27","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1109\/TPAMI.2011.170","volume":"34","author":"Y Yang","year":"2012","unstructured":"Yang Y, Nie F, Xu D, Luo J, Zhuang Y, Pan Y (2012) A multimedia retrieval framework based on semi-supervised ranking and relevance feedback. IEEE Trans Pattern Anal Mach Intell 34(4):723\u2013742","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3257_CR28","doi-asserted-by":"crossref","unstructured":"Yang Z, Awasthi M, Ghosh M, Mi N (2016) A fresh perspective on total cost of ownership models for flash storage in datacenters. In: 8th IEEE international conference on cloud computing technology and science (CloudCom 2016). IEEE","DOI":"10.1109\/CloudCom.2016.0049"},{"key":"3257_CR29","doi-asserted-by":"crossref","unstructured":"Yang Z, Tai J, Bhimani J, Wang J, Mi N, Sheng B (2016) Grem: Dynamic ssd resource allocation in virtualized storage systems with heterogeneous vms. In: 35th IEEE international performance computing and communications conference (IPCCC 2016). IEEE","DOI":"10.1109\/PCCC.2016.7820658"},{"issue":"10","key":"3257_CR30","doi-asserted-by":"publisher","first-page":"941","DOI":"10.1080\/08839514.2013.848753","volume":"27","author":"JC Yin","year":"2013","unstructured":"Yin JC, Wang NN (2013) Online grey prediction of ship roll motion using variable rbfn. Appl Artif Intell 27(10):941\u2013960","journal-title":"Appl Artif Intell"},{"key":"3257_CR31","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.oceaneng.2013.01.005","volume":"61","author":"JC Yin","year":"2013","unstructured":"Yin JC, Zou ZJ, Xu F (2013) On-line prediction of ship roll motion during maneuvering using sequential learning rbf neuralnetworks. Ocean Eng 61:139\u2013147","journal-title":"Ocean Eng"},{"key":"3257_CR32","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.neucom.2013.09.043","volume":"129","author":"JC Yin","year":"2014","unstructured":"Yin JC, Zou ZJ, Xu F, Wang NN (2014) Online ship roll motion prediction based on grey sequential extreme learning machine. Neurocomputing 129:168\u2013174","journal-title":"Neurocomputing"},{"key":"3257_CR33","doi-asserted-by":"publisher","unstructured":"Yin Z, Kong D, Shao G, Ning X, Jin W, Wang JY (2016) A-optimal convolutional neural network. Neural Comput Appl. doi:\n                    10.1007\/s00521-016-2783-9","DOI":"10.1007\/s00521-016-2783-9"},{"issue":"2","key":"3257_CR34","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1109\/TII.2016.2605629","volume":"13","author":"H Zhang","year":"2017","unstructured":"Zhang H, Cao X, Ho JK, Chow TW (2017) Object-level video advertising: an optimization framework. IEEE Trans Ind Inform 13(2):520\u2013531","journal-title":"IEEE Trans Ind Inform"},{"issue":"2","key":"3257_CR35","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1109\/TII.2016.2601521","volume":"13","author":"H Zhang","year":"2017","unstructured":"Zhang H, Li J, Ji Y, Yue H (2017) Understanding subtitles by character-level sequence-to-sequence learning. IEEE Trans Ind Inform 13(2):616\u2013624","journal-title":"IEEE Trans Ind Inform"},{"key":"3257_CR36","doi-asserted-by":"crossref","unstructured":"Zhang P, Kong X (2009) Detecting image tampering using feature fusion. In: International Conference on availability, reliability and security, 2009. ARES\u201909. IEEE, pp 335\u2013340","DOI":"10.1109\/ARES.2009.150"},{"key":"3257_CR37","doi-asserted-by":"crossref","unstructured":"Zhang P, Shi B, Smith CD, Liu J (2016) Nonlinear metric learning for semi-supervised learning via coherent point drifting. In: 2016 15th IEEE international conference on machine learning and applications (ICMLA). IEEE, pp 314\u2013319","DOI":"10.1109\/ICMLA.2016.0058"},{"issue":"4","key":"3257_CR38","doi-asserted-by":"publisher","first-page":"231","DOI":"10.14257\/ijmue.2015.10.4.22","volume":"10","author":"L Zhanying","year":"2015","unstructured":"Zhanying L, Jun X, Bo L, Jue W (2015) Prediction of ship roll motion based on optimized chaotic diagonal recurrent neural networks. Int J Multimed Ubiquitous Eng 10(4):231\u2013242","journal-title":"Int J Multimed Ubiquitous Eng"},{"key":"3257_CR39","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.ijmedinf.2016.09.014","volume":"97","author":"T Zheng","year":"2017","unstructured":"Zheng T, Xie W, Xu L, He X, Zhang Y, You M, Yang G, Chen Y (2017) A machine learning-based framework to identify type 2 diabetes through electronic health records. Int J Med Inform 97:120\u2013127","journal-title":"Int J Med Inform"},{"key":"3257_CR40","unstructured":"Zhou D, Weston J, Gretton A, Bousquet O, Sch\u00f6lkopf B (2004) Ranking on data manifolds. In: Advances in neural information processing systems, vol\u00a03, pp 169\u2013176"},{"issue":"2","key":"3257_CR41","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1109\/TKDE.2016.2562624","volume":"29","author":"L Zhu","year":"2017","unstructured":"Zhu L, Shen J, Xie L, Cheng Z (2017) Unsupervised visual hashing with semantic assistant for content-based image retrieval. IEEE Trans Knowl Data Eng 29(2):472\u2013486","journal-title":"IEEE Trans Knowl Data Eng"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-3257-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-017-3257-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-017-3257-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,1]],"date-time":"2019-08-01T13:35:46Z","timestamp":1564666546000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-017-3257-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,26]]},"references-count":41,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2019,7]]}},"alternative-id":["3257"],"URL":"https:\/\/doi.org\/10.1007\/s00521-017-3257-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2017,10,26]]},"assertion":[{"value":"23 April 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2017","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2017","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare no conflict of interest for the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}