乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T04:58:07Z","timestamp":1740113887730,"version":"3.37.3"},"reference-count":36,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2021KYQD23"],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012490","name":"North Minzu University","doi-asserted-by":"publisher","award":["2021JCYJ07"],"id":[{"id":"10.13039\/501100012490","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62366001","12361062"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004772","name":"Natural Science Foundation of Ningxia Province","doi-asserted-by":"publisher","award":["2023AAC02053"],"id":[{"id":"10.13039\/501100004772","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016692","name":"Key Research and Development Program of Ningxia","doi-asserted-by":"publisher","award":["2022BSB03046"],"id":[{"id":"10.13039\/100016692","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neural Networks"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1016\/j.neunet.2024.106262","type":"journal-article","created":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T21:11:23Z","timestamp":1710969083000},"page":"106262","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Distribution-free Bayesian regularized learning framework for semi-supervised learning"],"prefix":"10.1016","volume":"174","author":[{"given":"Jun","family":"Ma","sequence":"first","affiliation":[]},{"given":"Guolin","family":"Yu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neunet.2024.106262_b1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10107-002-0339-5","article-title":"Second-order cone programming","volume":"95","author":"Alizadeh","year":"2003","journal-title":"Mathematical Programming"},{"issue":"6","key":"10.1016\/j.neunet.2024.106262_b2","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/10556780903483356","article-title":"Interior proximal algorithm with variable metric for second-order cone programming: applications to structural optimization and support vector machines","volume":"25","author":"Alvarez","year":"2010","journal-title":"Optimization Methods & Software"},{"key":"10.1016\/j.neunet.2024.106262_b3","doi-asserted-by":"crossref","unstructured":"Bhattacharyya, Chiranjib (2004a). Robust classification of noisy data using second order cone programming approach. In International conference on intelligent sensing and information processing (pp. 433\u2013438).","DOI":"10.1109\/ICISIP.2004.1287696"},{"key":"10.1016\/j.neunet.2024.106262_b4","first-page":"1417","article-title":"Second order cone programming formulations for feature selection","volume":"5","author":"Bhattacharyya","year":"2004","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.neunet.2024.106262_b5","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.eswa.2016.01.044","article-title":"A second-order cone programming formulation for nonparallel hyperplane support vector machine","volume":"54","author":"Carrasco","year":"2016","journal-title":"Expert Systems with Applications"},{"issue":"6","key":"10.1016\/j.neunet.2024.106262_b6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10994-016-5616-2","article-title":"High-probability minimax probability machines","volume":"106","author":"Cousins","year":"2017","journal-title":"Machine Learning"},{"issue":"1","key":"10.1016\/j.neunet.2024.106262_b7","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.asoc.2012.08.003","article-title":"A minimax probabilistic approach to feature transformation for multi-class data","volume":"13","author":"Deng","year":"2013","journal-title":"Applied Soft Computing"},{"issue":"7","key":"10.1016\/j.neunet.2024.106262_b8","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.1109\/TNNLS.2016.2544779","article-title":"Structural minimax probability machine","volume":"28","author":"Gu","year":"2017","journal-title":"IEEE Transactions on Neural Networks & Learning Systems"},{"key":"10.1016\/j.neunet.2024.106262_b9","series-title":"Computer vision and pattern recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE computer society conference on","article-title":"Learning classifiers from imbalanced data based on biased minimax probability machine","author":"Huang","year":"2004"},{"key":"10.1016\/j.neunet.2024.106262_b10","first-page":"1253","article-title":"The minimum error minimax probability machine","volume":"5","author":"Huang","year":"2004","journal-title":"Journal of Machine Learning Research"},{"issue":"5","key":"10.1016\/j.neunet.2024.106262_b11","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/TPAMI.2007.1068","article-title":"Twin support vector machines for pattern classification","volume":"29","author":"Jayadeva","year":"2007","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"2","key":"10.1016\/j.neunet.2024.106262_b12","first-page":"1","article-title":"Dynamic minimax probability machine-based approach for fault diagnosis using pairwise discriminate analysis","volume":"27","author":"Jiang","year":"2017","journal-title":"IEEE Transactions on Control Systems Technology"},{"issue":"26","key":"10.1016\/j.neunet.2024.106262_b13","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1016\/j.neucom.2016.05.039","article-title":"Hessian semi-supervised extreme learning machine","volume":"207","author":"Krishnasamy","year":"2016","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.neunet.2024.106262_b14","first-page":"192","article-title":"Minimax probability machine","volume":"37","author":"Lanckriet","year":"2001","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2024.106262_b15","series-title":"International conference on neural information processing systems","first-page":"905","article-title":"Robust novelty detection with single-class MPM","author":"Lanckriet","year":"2002"},{"key":"10.1016\/j.neunet.2024.106262_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2018.04.005","article-title":"Robust twin support vector regression via second-order cone programming","author":"L\u00f3pez","year":"2018","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.neunet.2024.106262_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2019.06.014","article-title":"Twin minimax probability extreme learning machine for pattern recognition","volume":"187","author":"Ma","year":"2020","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.neunet.2024.106262_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2021.104550","article-title":"Regularized twin minimax probability machine for pattern classification and regression","volume":"107","author":"Ma","year":"2022","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"2","key":"10.1016\/j.neunet.2024.106262_b19","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s10489-016-0764-4","article-title":"A second-order cone programming formulation for twin support vector machines","volume":"45","author":"Maldonado","year":"2016","journal-title":"Applied Intelligence"},{"key":"10.1016\/j.neunet.2024.106262_b20","doi-asserted-by":"crossref","first-page":"2070","DOI":"10.1016\/j.patcog.2013.11.021","article-title":"Imbalanced data classification using second-order cone programming support vector machines","volume":"47","author":"Maldonado","year":"2014","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neunet.2024.106262_b21","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.neucom.2018.04.035","article-title":"Ellipsoidal support vector regression based on second-order cone programming","volume":"305","author":"Maldonado","year":"2018","journal-title":"Neurocomputing"},{"issue":"4","key":"10.1016\/j.neunet.2024.106262_b22","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.1214\/aoms\/1177705673","article-title":"Multivariate Chebyshev inequalities","volume":"31","author":"Marshall","year":"1960","journal-title":"The Annals of Mathematical Statistics"},{"issue":"15","key":"10.1016\/j.neunet.2024.106262_b23","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.1016\/j.patrec.2007.05.021","article-title":"A comparative study of minimax probability machine-based approaches for face recognition","volume":"28","author":"Ng","year":"2007","journal-title":"Pattern Recognition Letters"},{"key":"10.1016\/j.neunet.2024.106262_b24","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.knosys.2019.04.016","article-title":"Regularized minimax probability machine","volume":"177","author":"Sebasti\u00e1n","year":"2019","journal-title":"Knowledge-Based Systems"},{"issue":"6","key":"10.1016\/j.neunet.2024.106262_b25","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1109\/TNN.2011.2130540","article-title":"Improvements on twin support vector machines","volume":"22","author":"Shao","year":"2011","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"1","key":"10.1016\/j.neunet.2024.106262_b26","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/TMM.2014.2375792","article-title":"Hessian semi-supervised sparse feature selection based on L2,1\/2-matrix norm","volume":"17","author":"Shi","year":"2014","journal-title":"IEEE Transactions on Multimedia"},{"issue":"1","key":"10.1016\/j.neunet.2024.106262_b27","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/TSMC.2016.2563395","article-title":"Dimension reduction by minimum error minimax probability machine","volume":"47","author":"Song","year":"2017","journal-title":"IEEE Transactions on Systems Man & Cybernetics Systems"},{"key":"10.1016\/j.neunet.2024.106262_b28","first-page":"9","article-title":"A formulation for minimax probability machine regression","volume":"76","author":"Strohmann","year":"2003","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.neunet.2024.106262_b29","doi-asserted-by":"crossref","unstructured":"Wei, C., Sohn, K., Mellina, C., Yuille, A. L., & Yang, F. (2021). CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning. In 2021 IEEE\/CVF conference on computer vision and pattern recognition (pp. 10852\u201310861).","DOI":"10.1109\/CVPR46437.2021.01071"},{"issue":"99","key":"10.1016\/j.neunet.2024.106262_b30","first-page":"1","article-title":"Hessian semi-supervised scatter regularized classification model with geometric and discriminative information for nonlinear process","author":"Wei","year":"2021","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"2","key":"10.1016\/j.neunet.2024.106262_b31","doi-asserted-by":"crossref","first-page":"31","DOI":"10.14257\/ijhit.2015.8.2.03","article-title":"Twin minimax probability machine for handwritten digit recognition","volume":"8","author":"Xu","year":"2015","journal-title":"International Journal of Hybrid Information Technology"},{"key":"10.1016\/j.neunet.2024.106262_b32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2021.05.039","article-title":"Laplacian pair-weight vector projection for semi-supervised learning","volume":"573","author":"Xue","year":"2021","journal-title":"Information Sciences"},{"issue":"6","key":"10.1016\/j.neunet.2024.106262_b33","first-page":"493","article-title":"A new minimax probabilistic approach and its application in recognition the purity of hybrid seeds","volume":"104","author":"Yang","year":"2015","journal-title":"CMES. Computer Modeling in Engineering & Sciences"},{"issue":"8","key":"10.1016\/j.neunet.2024.106262_b34","doi-asserted-by":"crossref","first-page":"1914","DOI":"10.1039\/C5AY01304F","article-title":"Comparison of chemometric approaches for near-infrared spectroscopic data","volume":"8","author":"Yang","year":"2016","journal-title":"Analytical Methods"},{"key":"10.1016\/j.neunet.2024.106262_b35","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.neunet.2020.07.030","article-title":"Twin minimax probability machine for pattern classification","volume":"131","author":"Yang","year":"2020","journal-title":"Neural Networks"},{"issue":"37","key":"10.1016\/j.neunet.2024.106262_b36","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.patrec.2013.01.004","article-title":"Laplacian minimax probability machine","volume":"37","author":"Yoshiyama","year":"2014","journal-title":"Pattern Recognition Letters"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608024001862?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608024001862?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T18:57:53Z","timestamp":1731610673000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608024001862"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":36,"alternative-id":["S0893608024001862"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2024.106262","relation":{},"ISSN":["0893-6080"],"issn-type":[{"type":"print","value":"0893-6080"}],"subject":[],"published":{"date-parts":[[2024,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Distribution-free Bayesian regularized learning framework for semi-supervised learning","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2024.106262","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"106262"}}