{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,8,1]],"date-time":"2023-08-01T04:11:31Z","timestamp":1690863091311},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T00:00:00Z","timestamp":1667001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T00:00:00Z","timestamp":1667001600000},"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":["Neural Process Lett"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s11063-022-11073-4","type":"journal-article","created":{"date-parts":[[2022,10,29]],"date-time":"2022-10-29T07:04:37Z","timestamp":1667027077000},"page":"4933-4949","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Natural Threshold Model for Ordinal Regression"],"prefix":"10.1007","volume":"55","author":[{"given":"Xingyu","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanzhi","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhouwang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,29]]},"reference":[{"issue":"1","key":"11073_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-27569-w","volume":"8","author":"JL Causey","year":"2018","unstructured":"Causey JL, Zhang J, Ma S, Jiang B, Qualls JA, Politte DG, Prior F, Zhang S, Huang X (2018) Highly accurate model for prediction of lung nodule malignancy with ct scans. Sci Rep 8(1):1\u201312","journal-title":"Sci Rep"},{"issue":"3","key":"11073_CR2","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1162\/neco.2007.19.3.792","volume":"19","author":"W Chu","year":"2007","unstructured":"Chu W, Keerthi SS (2007) Support vector ordinal regression. Neural Comput 19(3):792\u2013815","journal-title":"Neural Comput"},{"issue":"1","key":"11073_CR3","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1109\/TKDE.2015.2457911","volume":"28","author":"PA Guti\u00e9rrez","year":"2015","unstructured":"Guti\u00e9rrez PA, Perez-Ortiz M, Sanchez-Monedero J, Fernandez-Navarro F, Hervas-Martinez C (2015) Ordinal regression methods: survey and experimental study. IEEE Trans Knowl Data Eng 28(1):127\u2013146","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"11073_CR4","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.neunet.2014.07.001","volume":"59","author":"PA Guti\u00e9rrez","year":"2014","unstructured":"Guti\u00e9rrez PA, Ti\u0148o P, Herv\u00e1s-Mart\u00ednez C (2014) Ordinal regression neural networks based on concentric hyperspheres. Neural Netw 59:51\u201360","journal-title":"Neural Netw"},{"key":"11073_CR5","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"11073_CR6","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/j.neucom.2018.07.102","volume":"396","author":"J de La Torre","year":"2020","unstructured":"de La Torre J, Valls A, Puig D (2020) A deep learning interpretable classifier for diabetic retinopathy disease grading. Neurocomputing 396:465\u2013476","journal-title":"Neurocomputing"},{"key":"11073_CR7","doi-asserted-by":"crossref","unstructured":"Li K, Xing J, Su C, Hu W, Zhang Y, Maybank S (2018) Deep cost-sensitive and order-preserving feature learning for cross-population age estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 399\u2013408","DOI":"10.1109\/CVPR.2018.00049"},{"issue":"2","key":"11073_CR8","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1109\/TIFS.2017.2746062","volume":"13","author":"H Liu","year":"2017","unstructured":"Liu H, Lu J, Feng J, Zhou J (2017) Label-sensitive deep metric learning for facial age estimation. IEEE Trans Inf Foren Security 13(2):292\u2013305","journal-title":"IEEE Trans Inf Foren Security"},{"key":"11073_CR9","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2020.01.025","volume":"388","author":"X Liu","year":"2020","unstructured":"Liu X, Fan F, Kong L, Diao Z, Xie W, Lu J, You J (2020) Unimodal regularized neuron stick-breaking for ordinal classification. Neurocomputing 388:34\u201344","journal-title":"Neurocomputing"},{"key":"11073_CR10","unstructured":"Liu Y, Kong AWK, Goh CK (2018) A constrained deep neural network for ordinal regression. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 831\u2013839"},{"key":"11073_CR11","doi-asserted-by":"crossref","unstructured":"Niu Z, Zhou M, Wang L, Gao X, Hua G (2016) Ordinal regression with multiple output CNN for age estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4920\u20134928","DOI":"10.1109\/CVPR.2016.532"},{"key":"11073_CR12","doi-asserted-by":"crossref","unstructured":"Pan H, Han H, Shan S, Chen X (2018) Mean-variance loss for deep age estimation from a face. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5285\u20135294","DOI":"10.1109\/CVPR.2018.00554"},{"key":"11073_CR13","unstructured":"Ricanek K, Tesafaye T (2006) Morph: A longitudinal image database of normal adult age-progression. In: 7th international conference on automatic face and gesture recognition (FGR06), pp. 341\u2013345. IEEE"},{"issue":"2\u20134","key":"11073_CR14","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1007\/s11263-016-0940-3","volume":"126","author":"R Rothe","year":"2018","unstructured":"Rothe R, Timofte R, Van Gool L (2018) Deep expectation of real and apparent age from a single image without facial landmarks. Int J Comput Vis 126(2\u20134):144\u2013157","journal-title":"Int J Comput Vis"},{"issue":"3","key":"11073_CR15","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211\u2013252","journal-title":"Int J Comput Vis"},{"key":"11073_CR16","doi-asserted-by":"crossref","unstructured":"Schifanella R, Redi M, Aiello LM (2015) An image is worth more than a thousand favorites: Surfacing the hidden beauty of flickr pictures. In: Proceedings of the international AAAI conference on web and social media, vol.\u00a09","DOI":"10.1609\/icwsm.v9i1.14612"},{"key":"11073_CR17","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1016\/j.neucom.2020.04.142","volume":"404","author":"Y Shu","year":"2020","unstructured":"Shu Y, Li Q, Liu S, Xu G (2020) Learning with privileged information for photo aesthetic assessment. Neurocomputing 404:304\u2013316","journal-title":"Neurocomputing"},{"key":"11073_CR18","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.neucom.2018.06.041","volume":"313","author":"Q Tian","year":"2018","unstructured":"Tian Q, Zhang W, Wang L, Chen S, Yin H (2018) Robust ordinal regression induced by lp-centroid. Neurocomputing 313:184\u2013195","journal-title":"Neurocomputing"},{"issue":"11","key":"11073_CR19","doi-asserted-by":"publisher","first-page":"2035","DOI":"10.1109\/TMM.2015.2479916","volume":"17","author":"X Tian","year":"2015","unstructured":"Tian X, Dong Z, Yang K, Mei T (2015) Query-dependent aesthetic model with deep learning for photo quality assessment. IEEE Trans Multimed 17(11):2035\u20132048","journal-title":"IEEE Trans Multimed"},{"key":"11073_CR20","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2020.03.034","volume":"401","author":"VM Vargas","year":"2020","unstructured":"Vargas VM, Guti\u00e9rrez PA, Herv\u00e1s-Mart\u00ednez C (2020) Cumulative link models for deep ordinal classification. Neurocomputing 401:48\u201358","journal-title":"Neurocomputing"},{"key":"11073_CR21","doi-asserted-by":"crossref","unstructured":"Zhang C, Liu S, Xu X, Zhu C (2019) C3ae: Exploring the limits of compact model for age estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 12587\u201312596","DOI":"10.1109\/CVPR.2019.01287"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11073-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-022-11073-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-022-11073-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T16:47:00Z","timestamp":1690822020000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-022-11073-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,29]]},"references-count":21,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["11073"],"URL":"https:\/\/doi.org\/10.1007\/s11063-022-11073-4","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,29]]},"assertion":[{"value":"17 October 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 October 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}