{“状态”:“确定”,“消息类型”:“工作”,“信息版本”:“1.0.0”,“邮件”:{“索引”:{“日期-部件”:[[2024,4,2],“日期-时间”:“2024-04-02T06:02:14Z”,“时间戳”:1712037734436},“参考-计数”:63,“出版商”:“Springer Science and Business Media LLC”“:”2021-06-26T00:00:00Z“,“timestamp”:1624665600000},“content-version”:“tdm”,“delay-in-days”:0,“URL”:“https:\/\/www.springer.com\/tdm”},{“start”:{“date-parts”:[[2021,6,26]],“date-time”:“2021-06-26T00:00:00Z”,“timetamp”:161466560000},.com/tdm“}],“出资人”:[{“DOI”:“10.13039\/100012542”,“名称”:“四川省科技支撑计划”,“doi-asserted-by”:“publisher”,“award”:[“2018GZDZX0031”]}:[“Neural Compute&Applic”],“published-print”:{“date-parts”:[[2021,12]]},“DOI”:“10.1007\/s00521-0621-w218-w”,“type”:“journal-article”,“created”:{“date-ports”:[2021,6,26]],“date-time”:“2021-06-26T07:02:30Z”,“timestamp”:1624690950000},”page“16181-16196”,“update-policy”:”http://\/dx。DOI.org\/10.1007\/springer_crossmark_policy“,”source“:”Crossref“,“is-referenced-by-count”:7,“title”:[“基于主题的标签分布学习利用标签歧义进行场景分类”],“prefix”:“10.1007”,“volume”:“33”,“author”:[{“ORCID”:”http://\/ORCID.org\/00000-0002-7432-1531“,”authenticated-ORCID“:false,”given“:”Jianqiao“,”family“:”Luo“:“He”,“sequence”:“additional”,“affiliation”:[]},{“given”:“Yang”,“family”:“Ou”,“serquence”:“additionable”,“abfiliation“:[]{”ORCID“:”http://\/ORCID.org\/00000-0003-1126-6165“,”authenticated-ORCID“:false,”given“:“Bailin”,“家庭”:“Li”,”sequence“:”additional“,“affaliation”:[]}.,{”givent“:”Kai“,”family“:“Wang”,”serquence“:”附加“,”从属“:[]}],“member”:“297”,“published-online”:{“date-parts”:[[2021,6,26]]},“reference”:[{“key”:“6218_CR1”,“doi-asserted-by”:“publisher”,《first page》:“1865”,“doi”:“10.1109\/JPROC.2017.2675998”,“volume”:”105“author”:“G Cheng”,“year”:“2017”,“unstructured”:“Cheng G,Han J,Lu X(2017)遥感影像场景分类:基准和状态艺术。Proc IEEE 105:1865\u20131883“,”journal-title“:”Proc EEE“},{”key“:”6218_CR2“,”doi-asserted-by“:”publisher“,”first page“:“3965”,”doi“:”10.1109\/TGRS.2017.2685945“,“volume”:“55”,“author”:“GS Xia”,“year”:“2017”,“unstructured”:“Xia GS,Hu J,Hu F et al(2017)”AID:空中场景分类性能评估的基准数据集。IEEE Trans Geosci远程传感器55:3965\u20133981。https:\/\/doi.org\/10.109\/TGRS.2017.2685945“,”journal-title“:”IEEE Trans Geosci Remote Sens“},{”key“:”6218_CR3“,”doi-asserted-by“:”publisher“,”first page“:“7894”,”doi“:”10.1109\/TGRS.2019.2917161“,”volume“57”,“author”:“X Lu”,“year”:“2019”,“unstructured”:“Lu X,Sun H,Zheng X(2019)一种用于遥感场景分类的特征聚合卷积神经网络。IEEE Trans Geosci远程传感器57:7894\u20137906。https:\/\/doi.org\/10.109\/tgrs.2019.2917161“,”journal-title“:”IEEE Trans Geosci Remote Sens“},{”key“:”6218_CR4“,”doi-asserted-by“:”publisher“,”first page“:“2055”,“doi”:“10.1109\/TIP.2017.2675339”,“volume”:“26”,“author”:“L Wang”,“year”:“2017”,“unstructured”:“Wang L,Guo S,Huang W et al(2017)”基于知识引导的多分辨率CNN大规模场景分类消歧。IEEE传输图像处理26:2055\u20132068。https:\/\/doi.org\/10.109\/TIP.2017.2675339“,”journal-title“:”IEEE Trans-Image Process“},{”key“:”6218_CR5“,”doi-asserted-by“:”publisher“,”unstructured“:”Lei Y,Dong Y,Xiong F et al(2018)模糊分类的模糊加权损失。参加:VCIP 2018-IEEE视觉通信和图像处理国际会议。https:\/\/doi.org/10.1109\/VCVP.2018.8698693“,”doi“:”10.1109\/VCVP.2018.8698693“},{”key“:”6218_CR6“,”doi断言“:”publisher“,”first page“:”1734“,”doi“:”10.1109\/TKDE.2016.2545658“,”volume“:”28“,”author“:”X耿“,”year“:”2016“,”nonstructured“:”耿X(2016)标签分发学习。IEEE Trans Knoll Data Eng 28:1734 \u20131748。https:\/\/doi.org\/10.109\/TKDE.2016.2545658“,”journal-title“:”IEEE Trans Knowl Data Eng“},{”key“:”6218_CR7“,”doi-asserted-by“:”publisher“,”first page“:“2825”,“doi”:“10.1109\/TIP.2017.2689998”,“volume”:“26”,“author”:“GB Bin”,“year”:“2017”,“unstructured”:“Bin GB,Xing C,Xie C et al(2017)具有标签模糊性的深度标签分布学习。IEEE传输图像处理26:2825\u20132838。https:\/\/doi.org\/10.109\/TIP.2017.2689998“,”journal-title“:”IEEE Trans-Image Process“},{“key”:“6218_CR8”,“doi-asserted-by”:“publisher”,“first page”:”5691“,”doi“:”10.1109\/TIP.2019.2922818“,”volume“:”28“,”author“:”M Ling“,”year“:”2019“,”unstructured“:”Ling M,Geng X(2019)室内人群计数通过混合高斯标签分布学习。IEEE传输图像处理28:5691\u20135701。https:\/\/doi.org\/10.109\/TIP.2019.2922818“,“journal-title”:“IEEE Trans-Image Process”},{“key”:“6218_CR9”,“doi-asserted-by”:“publisher”,“unstructured”:“杨J,Chen L,Zhang L等人(2018)通过标签分布学习对绘画图像进行基于历史上下文的风格分类。2018年MM——2018年ACM多媒体会议记录。第1154\u20131162页。https:\/\/doi.org\/10.1145\/3240508.3240593“,”doi“:”10.1145\/3240508.3240593“},{”key“:”6218_CR10“,”doi-asserted-by“:”publisher“,”unstructured“:”Gao BB,Zhou HY,Wu J,Geng X(2018)使用标签分布学习期望进行年龄估计。In:IJCAI国际人工智能联合会议。pp 712\u2013718。https:\/\/doi.org\/10.24963\/ijcai.2018\/99“,”doi“:”10.24963\/ijcai.2018“},{“key”:“6218_CR11”,“doi-asserted-by”:“publisher”,“unstructured”:“Wu X,Wen N,Liang J et al(2019)通过标签分布学习对痤疮图像进行联合分级和计数。摘自:IEEE计算机视觉国际会议论文集。pp 10641\u201310650。https:\/\/doi.org\/10.109\/ICCV.2019.01074“,”doi“:”10.1109\/ICCV.2019.01074“},{”key“:”6218_CR12“,”doi-asserted-by“:”publisher“,”first page“:“339”,”doi:“10.1016\/j.neucom.2018.12.074”,“volume”:“337”,“author”:“L Xu”,“year”:“2019”,“unstructured”:“Xu L,Chen j,Gan Y(2019)使用正则卷积神经网络的软标记头部姿势估计。神经计算337:339\u2013353。https:\/\/doi.org\/10.1016\/j.neucom.2018.12.074“,”journal-title“:”Neurocomputing“},{”key“:”6218_CR13“,”doi-asserted-by“:”publisher“,”unstructured“:”Liu Z,Chen Z,Bai j,et al(2019)通过标签分布的深度学习进行面部姿势估计。摘自:Proceedings-2019计算机视觉研讨会国际会议,2019年ICCVW。第1232\u20131240页。https:\/\/doi.org/10.1109\/ICCVW。2019.00156”,“doi”:“10.1109\/ICCVW。2019.00156”},{“key”:“6218_CR14”,“doi断言者”:“publisher”,“doi”:“10.1016\/j.patcog.2019.107178”,“author”:“P Li”,“year”:“2020”,“nonstructured”:“Li P,Hu Y,Wu X et al(2020)用于年龄估计的深度标签细化。模式识别。https:\/\/doi.org\/10.1016\/j.patcog.2019.107178“,”journal-title“:”Pattern Recognit“},{“key”:“6218_CR15”,“doi-asserted-by”:“publisher”,“first page”:”3846“,”doi“:”10.1109\/TIP.2017.2655445“,”volume“:”26“,”author“:”Z He“,“year”:“2017”,“unstructured”:“He Z,Li X,Zhang Z et al(2017)用于年龄估计的数据相关标签分布学习。IEEE传输图像处理26:3846\u20133858。https:\/\/doi.org\/10.109\/TIP.2017.2655445“,”journal-title“:”IEEE Trans-Image Process“},{”key“:”6218_CR16“,”doi-asserted-by“:”publisher“,”first page“:“55”,“doi”:“10.1109\/MSP.2010.938079”,“volume”:“27”,“author”:“D Blei”,“year”:“2010”,“unstructured”:“Blei D,Carin L,Dunson D(2010)概率主题模型。IEEE信号处理杂志27:55\u201365。https:\/\/doi.org\/10.109\/MSP.2010.938079“,”journal-title“:”IEEE Signal Process Mag“},{“key”:“6218_CR17”,“doi-asserted-by”:“publisher”,“first page”:”6207“,”doi“:”10.1109\/TGRS.2015.2435801“,”volume“:”53“,”author“:”Y Zhong“,”year“:”2015“,”unstructured“:”Zhong Y,Zhu Q,Zhang L(2015)基于多特征融合概率主题模型的高空间分辨率遥感影像场景分类。IEEE Trans Geosci远程传感器53:6207\u20136222。https:\/\/doi.org\/10.109\/TGRS.2015.2435801“,”journal-title“:”IEEE Trans Geosci Remote Sens“},{”key“:”6218_CR18“,”doi-asserted-by“:”publisher“,”doi“:”10.1145\/1889681.1889684“,”author“:”K Farrahi“,”year“:”2011“,”unstructured“:”Farrahi-K,Gatica-Perez D(2011)使用概率主题模型从大规模人类位置发现例程。ACM跨智能系统技术。https:\/\/doi.org\/10.1145\/1889681.1889684“,”journal-title“:”ACM Trans Intell Syst Technol“},{“key”:“6218_CR19”,“doi-asserted-by”:“publisher”,“doi”:“10.1145\/3290047”,“author”:“B Yuan”,“year”:“2019”,”unstructured“:”Yuan B,Gao X,Niu Z,Tian Q(2019)通过高斯潜在dirichlet分配和谱聚类发现潜在主题。ACM跨多用途计算机通信应用。https:\/\/doi.org\/10.1145\/3290047“,”journal-title“:”ACM Trans Multimed Compute Commun Appl“},{“key”:“6218_CR20”,“doi-asserted-by”:“publisher”,“first page”:”1393“,”doi“:”10.1109\/TIP.2017.2655449“,”volume“26”,“author”:“Y Wang”,“year”:“2017”,“unstructured”:“Wang Y,Lin X,Wu L,Zhang W(2017)有效的多查询扩展:协作深度网络实现稳健的地标检索。IEEE传输图像处理26:1393\u20131404。https:\/\/doi.org\/10.109\/TIP.2017.2655449“,”journal-title“:”IEEE Trans-Image Process“},{”key“:”6218_CR21“,”doi-asserted-by“:”publisher“,”first page“:“2801”,“doi”:“10.1109\/TMM.2018.2812605”,“volume”:“20”,“author”:“J Zhang”,“year”:“2018”,“unstructured”:“Zhang J,Wu Q,Shen C et al(2018)具有区域潜在语义依赖的多标签图像分类。IEEE Trans Multimed 20:2801\u20132813。https:\/\/doi.org/10.1109\/TTM.2018.2812605“,”期刊标题“:”IEEE跨多模式“},{”键“:”6218_CR22“,”doi断言“:”出版商“,”非结构化“:”Hua Y,Mou L,Zhu XX(2019)多标签航空图像分类的标签关系推断。在:国际地球科学与遥感研讨会(IGARSS)。第5244\u20135247页。https:\/\/doi.org\/10.109\/IGARSS.2019.8898934“,”doi“:”10.1109\/IGARSAS.2019.898934“},{”key“:”6218_CR23“,”doi-asserted-by“:”publisher“,”first page“:“538”,“doi”:“10.1109\/TMC.2014.2322373”,“volume”:“14”,“author”:“Z Wang”,“year”:“2015”,“unstructured”:“Wang Z,Liao J,Cao Q et al(2015)Friendbook:一个基于语义的社交网络好友推荐系统。IEEE Trans Mob计算14:538\u2013551。https:\/\/doi.org\/10.109\/TMC.2014.2322373“,”journal-title“:”IEEE Trans-Mob Compute“},{”key“:”6218_CR24“,”doi-asserted-by“:”publisher“,”unstructured“:”Pan T,Zhang W,Wang Z,Xu L(2016)基于android应用程序中LDA主题模型的建议。摘自:2016年IEEE软件质量、可靠性和安全国际会议论文集,QRS-C 2016。https:\/\/doi.org\/10.109\/QRS-C.2016.24“,”doi“:”10.1109\/QRS.C.2016.24“},{”key“:”6218_CR25“,”doi-asserted-by“:”publisher“,”first page“:“28”,“doi”:“10.1016\/j.dss.2017.05.013”,“volume”:“101”,“author”:“CY Sun”,“year”:“2017”,“unstructured”:“Sun CY,Lee AJT(2017)”通过挖掘照片共享社交媒体提供旅游推荐。Decis支持系统101:28\u201339。https:\/\/doi.org\/10.1016\/j.dss.2017.05.013“,”journal-title“:”Decis Support Syst“},{“key”:“6218_CR26”,“doi-asserted-by”:“publisher”,“first page”:”224“,”doi“:”10.1109\/TMM.2017.2716829“,”volume“:”20“,”author“:”j Yao“,”year“:”2018“,”unstructured“:”Yao j,Wang Y,Zhang Y et al(2018)社交标签的联合潜在dirichlet分配。IEEE Trans Multimed 20:224\u2013237。https:\/\/doi.org\/10.109\/TMM.2017.2716829“,”journal-title“:”IEEE Trans-Multimed“},{“key”:“6218_CR27”,“doi-asserted-by”:“publisher”,“first page”:”9307“,”doi“:”10.1007\/s00521-019-04337-z“,”volume“:”31“,”author“:”Y Ou“,”year“:”2019“,”unstructured“:”Ou Y,Luo J,Li B,He B(2019)基于计算机视觉的铁路扣件分类模型。神经计算应用31:9307\u20139319。https:\/\/doi.org\/10.1007\/s00521-019-04337-z“,”journal-title“:”Neural Compute Appl“},{“key”:“6218_CR28”,“doi-asserted-by”:“publisher”,“first page”:”65“,”doi“:”10.1016\/j.neucom.2018.06.014“,”volume“:”314“,“author”:“Y Li”,”year“:”2018“,”unstructured“:”Li Y,Kong X,Fu H,Tian Q(2018)聚合用于图像检索的分层二进制激活。神经计算314:65\u201377。https:\/\/doi.org\/10.1016\/j.neucom.2018.06.014“,”journal-title“:”Neurocomputing“},{”key“:”6218_CR29“,”doi-asserted-by“:”publisher“,”first-page“:”459“,”doi“:”10.1109\/LGRS.2018.2794511“,”volume“:“15”,“author”:“R Bahmanyar”,“year”:“2018”,“unstructured”:“Bahmanyar R,Espinoza-Molina D,Datcu M(2018)基于多模态潜在Dirichlet分配模型的多传感器对地观测图像分类。IEEE Geosci遥感快报15:459\u2013463。https:\/\/doi.org\/10.109\/LGRS.2018.2794511“,”journal-title“:”IEEE Geosci Remote Sens Lett“},{”key“:”6218_CR30“,”doi-asserted-by“:”publisher“,”first page“:“2600”,”doi“:”10.1109\/JSTARS.2018.2878037“,”volume“:”12“,”author“:”P Du“,“year”:“2019”,”unstructured“:”Du P,Li E,Xia J et al(2019)用于遥感场景分类的预处理CNN特征和模型级融合。IEEE J Sel Top Appl Earth Obs遥感器12:2600\u20132611。https:\/\/doi.org\/10.109\/JSTARS.2018.2878037“,”journal-title“:”IEEE J Sel Top Appl Earth Obs Remote Sens“},{“key”:“6218_CR31”,“doi-asserted-by”:“publisher”,“doi”:“10.1007\/s00521-05071-7”,”author“:”B Yuan“,”year“2020”,“unstructured”:“Yuan B,Han L,Gu X et al(2020)”用于高分辨率遥感图像场景分类的多深度特征融合。神经计算应用。https:\/\/doi.org\/10.1007\/s00521-020-05071-7“,“journal-title”:“Neural Comput Appl”},{“key”:“6218_CR32”,“doi-asserted-by”:“publisher”,“unstructured”:“Das P,Xu C,Doell RF,Corso JJ(2013)只需几个词就可以完成1000帧:通过潜在主题和稀疏对象拼接对视频进行语言描述。收录:IEEE计算机学会计算机视觉和模式识别会议论文集。第2634\u20132641页。https:\/\/doi.org\/10.109\/CVPR.2013.340“,”doi“:”10.1109\/CVPR.2013.340“},{”key“:”6218_CR33“,”doi-asserted-by“:”publisher“,”first page“:“2324”,”doi:“10.1109\/TIP.2008.2006658”,“volume”:“17”,“author”:“M Zhang”,“year”:“2008”,“unstructured”:“Zhang M,Gunturk BK(2008)用于图像去噪的多分辨率双边滤波。IEEE Trans-Image过程17:2324\u20132333。https:\/\/doi.org\/10.109\/TIP.2008.2006658“,”journal-title“:”IEEE Trans-Image Process“},{“key”:“6218_CR34”,“unstructured”:“Hessam B,Maxwell H,Mohammad R et al(2018)标签精炼厂通过标签级数改进ImageNet分类。In:IEEE计算机视觉和模式识别学会会议。arXiv:1805.02641”},}“key“:”6218_CR35“,”unstructured“:”M\u00fcller R,Kornblith S,Hinton G(2019)标签平滑何时起作用?在:神经信息处理系统研讨会,NIPS 2019,会议记录。arXiv:1906.02629“},{“key”:“6218_CR36”,“doi-asserted-by”:“publisher”,“first-pages”:“15”,“doi”:“10.1016\/j.neucom.2018.11.088”,“volume”:”345“,“author”:“j Hou”,“year”:“2019”,“unstructured”:“Hou j,Zeng H,Cai L等人(2019)多标签平滑正则化多标签学习用于车辆再识别。神经计算345:15\u201322。https:\/\/doi.org/10.1016\/j.neucom.2018.11.088”,“期刊标题”:“神经计算”},{“密钥”:“6218_CR37”,“doi断言”:“发布者”,“非结构化”:“Yun S,Park j,Lee K,Shin j(2020)通过自知蒸馏规范类预测。收录:IEEE计算机学会计算机视觉和模式识别会议论文集。第13873页\u201313882。https:\/\/doi.org\/10.109\/CVPR42600.2020.01389“,”doi“:”10.1109\/CVPRS2600.2020.01389“},{”key“:”6218_CR38“,”unstructured“:”Pereyra G,Tucker G,Chorowski J等人(2017)通过惩罚自信的输出分布来规范神经网络。参加:第五届学习代表国际会议,2017年国际劳工大会,会议记录。arXiv:1701.06548“},{“key”:“6218_CR39”,“doi-asserted-by”:“publisher”,“unstructured”:“Szegedy C,Vanhoucke V,Ioffe S et al(2016)重新思考计算机视觉的初始架构。摘自:IEEE计算机学会计算机视觉和模式识别会议论文集。pp 2818\u20132826。https:\/\/doi.org\/10.109\/CVPR.2016.308“,”doi“:”10.1109\/CVPR.2016.308“},{”key“:”6218_CR40“,”doi-asserted-by“:”publisher“,”doi“:“10.1109\/TGRS.2018.2845668”,”author“:”N He“,“year”:“2018”,”unstructured“:”He N,Fang L,Li S et al(2018)使用多层叠加协方差池进行遥感场景分类。IEEE Trans Geosci Remote Sens.https:\/\/doi.org\/10.109\/TGRS.2018.2845668“,”journal-title“:”IEEE Trans-Geosci远程传感器“},{“key”:“6218_CR41”,”doi-asserted-by“:”publisher“,”first page“:“2494”,”doi“:”10.1109\/TGRS.2018.2873966“,”volume“:”57“,”author“:”Y Liu“,”year“2019”,”unstructured“:”Liu Y,Suen CY,Liu Y丁磊(2019)使用分级wasserstein CNN进行场景分类。IEEE Trans Geosci远程传感器57:2494\u20132509。https:\/\/doi.org\/10.109\/TGRS.2018.2873966“,”journal-title“:”IEEE Trans Geosci Remote Sens“},{”key“:”6218_CR42“,”doi-asserted-by“:”publisher“,”first page“:“1779”,”doi“:”10.1109\/TGRS.2018.2869101“,”volume“57”,”author“:”Y Yuan“,”year“:”2019“,”unstructured“:”Yuan,Fang J,Lu X et al(2019)利用重新排列的局部特征进行遥感图像场景分类。IEEE Trans Geosci远程传感器57:1779\u20131792。https:\/\/doi.org\/10.109\/TGRS.2018.2869101“,“journal-title”:“IEEE Trans Geosci Remote Sens”},{“key”:“6218_CR43”,“doi-asserted-by”:“publisher”,“doi”:“10.3390\/rs11050494”,“author”:“W Zhang”,“year”:“2019”,“unstructured”:“Zhang W,Tang P,Zhao L et al(2019)使用CNN CapsNet进行遥感图像场景分类。Remote Sens.https:\/\/doi.org\/10.3390\/rs11050494“,”journal-title“:”Remote Sens“},{“key”:“6218_CR44”,“doi-asserted-by”:“publisher”,“first page”:”345“,”doi“:”10.1016\/j.neucom.2019.11.068“,”volume“:”377“,”author“:”Q Bi“,”year“:”2020“,”unstructured“:”Bi Q,Qin K,Zhang H et al(2020)RADC-Net:用于空中场景分类的基于剩余注意力的卷积网络。神经计算377:345\u2013359。https:\/\/doi.org\/10.1016\/j.neucom.2019.11.068“,”journal-title“:”Neurocomputing“},{”key“:”6218_CR45“,”doi-asserted-by“:”publisher“,”first-page:“220”,“doi”:“10.1109\/JSTARS.2017.2761800”,“volume”:“11”,“author”:“Y Liu”,“year”:“2018”,“unstructured”:“Liu Y,Huang C(2018)通过三重网络进行场景分类。IEEE J Sel Top Appl Earth Obs遥感器11:220\u2013237。https:\/\/doi.org\/10.109\/JSTARS.2017.2761800“,”journal-title“:”IEEE J Sel Top Appl Earth Obs Remote Sens“},{“key”:“6218_CR46”,“doi-asserted-by”:“publisher”,“first page”:”6916“,”doi“:”10.1109\/TGRS.2019.2909695“,”volume“:”57“,”author“:”J Xie“,”year“2019”,“unstructured”:“Xie J,He N,Fang L et al(2019)无标度卷积神经网络在遥感场景分类中的应用。IEEE Trans Geosci远程传感器57:6916\u20136928。https:\/\/doi.org\/10.109\/TGRS.2019.2909695“,”journal-title“:”IEEE Trans Geosci Remote Sens“},{”key“:”6218_CR47“,”doi-asserted-by“:”publisher“,”first page“:“519”,“doi”:“10.1109\/TGRS.2019.2937830”,“volume”:“58”,“author”:“Y Yu”,“year”:“2020”,“unstructured”:“Yu Y,Li X,Liu F(2020)注意GANs:用于航空场景分类的无监督深层特征学习。IEEE Trans Geosci远程传感器58:519\u2013531。https:\/\/doi.org\/10.109\/TGRS.2019.2937830“,“journal-title”:“IEEE Trans-Geosci遥感”},{“key”:“6218_CR48”,“unstructured”:“Simonyan K,Zisserman A(2015)用于大规模图像识别的深度卷积网络。参加:2015年ICLR第三届学习代表国际会议——会议记录。arXiv:1409.1556.“},{“key”:“6218_CR49”,“首页”:“2579”,“卷”:“9”,“作者”:“Maaten L.v.d,Hinton G,”,“年份”:“2008”,“非结构化”:“Maaten L.v.d,Hinton G(2008)使用t-SNE可视化数据。J Mach Learn Res 9:2579\u20132625”,“期刊标题”:“J Mach Learn Res”},{“key”:“6218_CR50”,“doi断言”:“publisher”,“doi“:”10.1145\/1961189.1961199“,“author”:“CC Chang”,“year”:“2011”,“unstructured”:“Chang CC,Lin CJ(2011)LIBSVM:支持向量机库。ACM跨智能系统技术。https:\/\/doi.org\/10.1145\/1961189.1961199“,”journal-title“:”ACM Trans Intell Syst Technol“},{“key”:“6218_CR51”,“unstructured”:“Stevens K,Kegelmeyer P,Andrzejewski D,Butter D(2012)在多个模型和多个主题上探索主题连贯性。在:EMNLP-CoNLL 2012-2012年自然语言处理和计算自然语言学习经验方法联合会议,会议记录。pp 952\u2013961“},{“key”:“6218_CR52”,“doi asserted by”:“publisher”,“first page”:“156”,“doi”:“10.1016\/j.in.2019.01.024”,“volume”:“482”,“author”:“S Chen”,“year”:“2019”,“nonstructured”:“Chen S,Wang Y,Lin C et al(2019)半监督特征学习,用于改进作家识别。Inf Sci(Ny)482:156\u2013170。https:\/\/doi.org\/10.1016\/j.ins.2019.01.024“,”journal-title“:”Inf-Sci(Ny)“},{”key“:”6218_CR53“,”doi-asserted-by“:”publisher“,”doi“:”10.1002\/rcs.2169“,”author“:”Y Du“,“year”:“2020”,”unstructured“:”Du Y,Yang R,Chen Z et al(2020)基于Caffe深度学习框架和EasyDL平台的膀胱镜下深度学习网络辅助膀胱肿瘤识别。Int J Med Robot Compute Assist Surg.https:\/\/doi.org\/10.1002\/rcs.2169“,”journal-title“:”Int J Med Robot Computer Assist Suprg“},{“key”:“6218_CR54”,“doi-asserted-by”:“publisher”,“doi”:“10.1016\/J.compbiomed.2020.103861”,“author”:“Y Zeng”,“year”:“2020”,“unstructured”:“Zeng Y,Zhang J(2020)使用Google Cloud AutoML Vision检测浸润性导管癌的机器学习模型。Comput Biol Med.https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103861“,”journal-title“:”Comput Biol Med“},{”key“:”6218_CR55“,”doi-asserted-by“:”publisher“,”unstructured“:”Chen Z,Wei X,Wang P,Guo Y(2019)基于图卷积网络的多标签图像识别。收录:IEEE计算机学会计算机视觉和模式识别会议论文集。第5172\u20135181页。https:\/\/doi.org\/10.109\/CVPR.2019.00532“,”doi“:”10.1109\/CVPR.2019.00532“},{”key“:”6218_CR56“,”doi-asserted-by“:”publisher“,”first page“:“4689”,“doi”:“10.1007\/s00521-018-3817-2”,“volume”:“32”,“author”:“T Zhang”,“year”:“2020”,“unstructured”:“Zhang T,Mouch\u00e8re H,Viard-Gaud单位:C(2020年)基于树BLSTM的在线手写数学表达式识别系统。神经计算应用程序32:4689\u20134708。https:\/\/doi.org\/10.1007\/s00521-018-3817-2“,”journal-title“:”Neural Compute Appl“},{“key”:“6218_CR57”,“doi-asserted-by”:“publisher”,“doi”:“10.1016\/j.patcog.2020.107596”,“author”:“Y Liu”,“year”:“2021”,“unstructured”:“Liu Y,Chen W,Qu H et al(2021)”图卷积网络的弱监督图像分类和逐点定位。模式识别。https:\/\/doi.org\/10.1016\/j.patcog.2020.107596“,”journal-title“:”Pattern Recognit“},{”key“:”6218_CR58“,”doi-asserted-by“:”publisher“,”doi“:”10.1007\/s00521-05650-8“,“author”:“K Xie”,“year”:“2021”,“unstructured”:“Xie K,Wei Z,Huang L et al(2021)”关注多标签天气识别的图卷积网络。神经计算应用。https:\/\/doi.org\/10.1007\/s00521-05-20-05650-8“,”journal-title“:”Neural Comput Appl“},{“key”:“6218_CR59”,“unstructured”:“Hinton G,Vinyals O,Dean J.(2015)《提取神经网络中的知识》。in:Neural information processing systems workshops,NIPS 2015,会议记录。arXiv:1503.02531“}”,{”key“:”6218_CR160“,”doi由“:”出版商“,”doi“:”10.1109\/TPAMI.20213005564“,”作者“:”L Wang“,”年份“:”2021“,”非结构化“:”Wang L,Yoon KJ(2021)视觉智能的知识提炼和师生学习:综述和新观点。IEEE Trans-Pattern Ana Mach Intell公司。https:\/\/doi.org\/10.109\/TPAMI.2021.3055564“,”journal-title“:”IEEE Trans-Pattern Ana Mach Intell“},{“key”:“6218_CR61”,“doi-asserted-by”:“publisher”,“doi”:“10.1016\/j.patcog.2020.107722”,“author”:“Z Wang”,“year”:“2021”,“unstructured”:“Wang Z,Du j(2021)”CNN中用于中文文本识别的联合架构和知识提取。模式识别。https:\/\/doi.org\/10.1016\/j.patcog.2020.107722“,”journal-title“:”Pattern Recognit“},{”key“:”6218_CR62“,”doi-asserted-by“:”publisher“,”unstructured“:”Yuan L,Tay FEH,Li G et al(2020)通过标签平滑正则化重新访问知识提取。收录:IEEE计算机学会计算机视觉和模式识别会议论文集。第3902\u20133910页。https:\/\/doi.org\/10.109\/CVPR42600.2020.00396“,”doi“:”10.1109\/CVPR12600.2020.00396“},{”key“:”6218_CR63“,”doi-asserted-by“:”publisher“,”unstructured“:”Zhang Y,Xiang T,Hospedales TM,Lu H(2018)深度相互学习。In:IEEE计算机视觉和模式识别学会会议记录。pp 4320\u20134328。https:\/\/doi.org\/10.109\/CVPR.2018.00454“,”doi“:”10.1109\/CVPR.2018.00454“}],”container-title“:[”Neural Computing and Applications“],”original-title”:[],”language“:”en“,”link“:[{”URL“:”https:\//link.springer.com\/content\/pdf\/10007\/s00521-06218-w.pdf“,”content-type“:”application\/pdf“、”content-version“:”vor“,”预期应用程序“:”text-mining“},{“URL”:“https:\/\/link.springer.com/article\/10.1007\/s00521-06218-w\/fulltext.html”,“内容类型”:“text\/html”,“content-version”:“vor”,“intended-application”:“text-mining”},}“URL“:”https:\//link.springer.com/content\/pdf\/10.10007\/s005211-06218w.pdf“,”内容类型“:”application\/pdf“content-v.pdf”版本“:”vor“,”intended-application“:”similarity-checking“}],”deposed“:{”date-parts“:[2021,11,3]],”date-time“:”2021-11-03T18:13:20Z“,”timestamp“:1635963200000},”score“:1,”resource“:{primary”:{“URL”:“https:\/\/link.springer.com\/10007\/s00521-06218-w“}}”,”subtitle“:[],”shorttitle“:[],”issued“:{”date-ports“:[[2020]1,6,26]]},“引用计数”:63,“日志-期刊“:{”期刊“:“23”,“published-print”:{“date-parts”:[[2021,12]]}},“alternative-id”:[“6218”],“URL”:“http://\/dx.doi.org\/10.1007\/s00521-06218-w”,“relationship”:{},”ISSN“:[”0941-0643“,”1433-3058“],”ISSN-type“:[{”value“:”0941-0.643“”,“type”:“print”},{“值”:“1433-3058”,“类型”:“电子”}],“主题”:[],“发布”:{“日期部分”:[[2021,6,26]]},“断言”:[{“value”:“2020年8月5日”,“order”:1,“name”:“received”,“label”:“已接收”,“group”:{“name”:“ArticleHistory”,“table”:“Article History“}},{“value”:“2021年6月8日”:“first_online”,“label”:“First Online”,“group”:{“name”:“Article History”,“label”:“Article Hiology”}},{“order”:1,“name”:“Ethics”,“group”:{“name”∶“EthicsHeading”,“table”:“Declarations”},}“value”:“作者声明他们没有利益冲突。”,“order“:2,”name“:”Ethics“,”group“:{”name“:”EthicsHeading“,”label“:”利益冲突“}}}]}