乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,7]],"date-time":"2024-07-07T23:54:57Z","timestamp":1720396497540},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876154","61876155"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu","doi-asserted-by":"publisher","award":["BK20181190"],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2020,9]]},"DOI":"10.1016\/j.neucom.2019.08.101","type":"journal-article","created":{"date-parts":[[2020,3,10]],"date-time":"2020-03-10T17:07:30Z","timestamp":1583860050000},"page":"82-90","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Improving deep neural network performance by integrating kernelized Min-Max objective"],"prefix":"10.1016","volume":"408","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-0918-4606","authenticated-orcid":false,"given":"Qiu-Feng","family":"Wang","sequence":"first","affiliation":[]},{"given":"Kai","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Amir","family":"Hussain","sequence":"additional","affiliation":[]},{"given":"Kaizhu","family":"Huang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2019.08.101_bib0001","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neucom.2016.12.038","article-title":"A survey of deep neural network architectures and their applications","volume":"234","author":"Liu","year":"2017","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2019.08.101_bib0002","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.neucom.2019.08.101_sbref0003","series-title":"Deep Learning: Fundamentals, Theory and Applications","author":"Huang","year":"2019"},{"key":"10.1016\/j.neucom.2019.08.101_bib0004","series-title":"In NIPS","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"Krizhevsky","year":"2012"},{"key":"10.1016\/j.neucom.2019.08.101_bib0005","series-title":"In ICLR","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2015"},{"key":"10.1016\/j.neucom.2019.08.101_bib0006","series-title":"In CVPR","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"issue":"1","key":"10.1016\/j.neucom.2019.08.101_bib0007","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/TASL.2011.2134090","article-title":"Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition","volume":"20","author":"Dahl","year":"2012","journal-title":"IEEE Trans. Audio, Speech Lang. Process"},{"key":"10.1016\/j.neucom.2019.08.101_bib0008","series-title":"Proc. Interspeech","doi-asserted-by":"crossref","first-page":"3429","DOI":"10.21437\/Interspeech.2016-1033","article-title":"Advances in very deep convolutional neural networks for LVCSR","author":"Sercu","year":"2016"},{"key":"10.1016\/j.neucom.2019.08.101_bib0009","doi-asserted-by":"crossref","unstructured":"A. Zeyer, K. Irie, R. Schlter, H. Ney, Improved training of end-to-end attention models for speech recognition, 2018, ArXiv:1805.03294.","DOI":"10.21437\/Interspeech.2018-1616"},{"key":"10.1016\/j.neucom.2019.08.101_bib0010","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.21437\/Interspeech.2010-343","article-title":"Recurrent neural network based language model","volume":"2","author":"Mikolov","year":"2010","journal-title":"Interspeech"},{"key":"10.1016\/j.neucom.2019.08.101_bib0011","unstructured":"T. Young, D. Hazarika, S. Poria, E. Cambria, Recent trends in deep learning based natural language processing, 2018, ArXiv:1708.02709v8."},{"key":"10.1016\/j.neucom.2019.08.101_bib0012","doi-asserted-by":"crossref","unstructured":"X. He, L. Liao, H. Zhang, L..Nie, X. Hu, T. Chua, Neural collaborative filtering, 2017, WWW, pp. 173\u2013182.","DOI":"10.1145\/3038912.3052569"},{"key":"10.1016\/j.neucom.2019.08.101_bib0013","series-title":"AAAI","article-title":"DeepCF: a unified framework of representation learning and matching function learning in recommender system","author":"Deng","year":"2019"},{"key":"10.1016\/j.neucom.2019.08.101_sbref0011","series-title":"Pattern Classification","author":"Duda","year":"2001"},{"issue":"11","key":"10.1016\/j.neucom.2019.08.101_bib0015","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"issue":"7","key":"10.1016\/j.neucom.2019.08.101_bib0016","first-page":"2872","article-title":"Improving CNN performance accuracies with MinMax objective","volume":"29","author":"Shi","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.neucom.2019.08.101_sbref0014","series-title":"Advances in Multimedia Information Processing - PCM","article-title":"Integrating supervised laplacian objective with CNN for object recognition","author":"Shi","year":"2016"},{"key":"10.1016\/j.neucom.2019.08.101_bib0018","series-title":"International Joint Conference on Artificial Intelligence (IJCAI), pp. 2004 - 2010","article-title":"Improving CNN performance with min-max objective","author":"Shi","year":"2016"},{"key":"10.1016\/j.neucom.2019.08.101_bib0019","series-title":"European Conference on Computer Vision (ECCV), pp. 311 - 327","article-title":"Transductive semi-supervised deep learning using min-max features","author":"Shi","year":"2018"},{"issue":"1","key":"10.1016\/j.neucom.2019.08.101_bib0020","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/TPAMI.2007.250598","article-title":"Graph enbedding and extensions:a general framework for dimensionality reduction","volume":"29","author":"Yan","year":"2007","journal-title":"IEEE Trans. PAMI"},{"key":"10.1016\/j.neucom.2019.08.101_bib0021","series-title":"International Joint Conference on Neural Networks (IJCNN), Pp. 1622\u20131629","article-title":"Integrating supervised subspace criteria with restricted boltzmann machine for feature extraction","author":"Xie","year":"2014"},{"key":"10.1016\/j.neucom.2019.08.101_bib0022","first-page":"558","article-title":"Learning classifiers from imbalanced data based on biased minimax probability machine","volume":"2","author":"Huang","year":"2004","journal-title":"Proc. CVPR"},{"key":"10.1016\/j.neucom.2019.08.101_sbref0020","series-title":"Machine Learning: Modeling Data Locally and Gloablly","author":"Huang","year":"2008"},{"key":"10.1016\/j.neucom.2019.08.101_bib0024","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/TNN.2007.905855","article-title":"Maxi-min margin machine: learning large margin classifiers globally and locally","volume":"19","author":"Huang","year":"2008","journal-title":"IEEE Trans. Neural Networks"},{"key":"10.1016\/j.neucom.2019.08.101_bib0025","series-title":"ICONIP, pp. 182\u2013191","article-title":"Improving deep neural network performance with kernelized min-max objective","author":"Yao","year":"2018"},{"key":"10.1016\/j.neucom.2019.08.101_bib0026","series-title":"Proceedings of the International Conference on Machine Learning (ICML), pp. 507\u2013516","article-title":"Large-margin softmax loss for convolutional neural networks","author":"Liu","year":"2016"},{"issue":"4","key":"10.1016\/j.neucom.2019.08.101_bib0027","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1142\/S0218001493000339","article-title":"Signature verification using a siamese time delay neural network","volume":"7","author":"Bromley","year":"1993","journal-title":"Int. J. Pattern Recognit. Artif. Intell. (IJPRAI)"},{"key":"10.1016\/j.neucom.2019.08.101_bib0028","series-title":"Pattern Recognition","article-title":"Learning by coincidence: siamese networks and common variable learning","author":"Shaham","year":"2017"},{"key":"10.1016\/j.neucom.2019.08.101_bib0029","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815\u2013823","article-title":"Facenet: a unified embedding for face recognition and clustering","author":"Schroff","year":"2015"},{"key":"10.1016\/j.neucom.2019.08.101_bib0030","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2167\u20132175","article-title":"Deep relative distance learning: tell the difference between similar vehicles","author":"Liu","year":"2016"},{"key":"10.1016\/j.neucom.2019.08.101_bib0031","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3474\u20133482","article-title":"Robust classification with convolutional prototype learning","author":"Yang","year":"2018"},{"key":"10.1016\/j.neucom.2019.08.101_bib0032","series-title":"Proceedings of the IEEE International Conference on Data Mining (ICDM), pp. 301\u2013309","article-title":"A unified gradient regularization family for adversarial examples","author":"Lyu","year":"2015"},{"key":"10.1016\/j.neucom.2019.08.101_bib0033","unstructured":"K. Huang, R. Jin, Z. Xu, C.L. Liu, Robust metric learning by smooth optimization, 2012, ArXiv:1203.3461."},{"key":"10.1016\/j.neucom.2019.08.101_bib0034","unstructured":"S. Zhang, K. Huang, J. Zhu, Y. Liu, Manifold adversarial learning, 2018, ArXiv:1807.05832."},{"issue":"7","key":"10.1016\/j.neucom.2019.08.101_bib0035","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"issue":"5786","key":"10.1016\/j.neucom.2019.08.101_bib0036","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"issue":"1","key":"10.1016\/j.neucom.2019.08.101_bib0037","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.neucom.2019.08.101_bib0038","series-title":"Proceedings of the 30 th International Conference on Machine Learning (ICML), Atlanta, Georgia, USA, PMLR","first-page":"1058","article-title":"Regularization of neural networks using dropconnect","volume":"28","author":"Wan","year":"2013"},{"key":"10.1016\/j.neucom.2019.08.101_bib0039","series-title":"Proceedings of the International Conference on Machine Learning (ICML), pp. 807\u2013814","article-title":"Rectified linear units improve restricted Boltzmann machines","author":"Nair","year":"2010"},{"key":"10.1016\/j.neucom.2019.08.101_bib0040","series-title":"Proceedings of the International Conference on Machine Learning (ICML)","article-title":"Rectifier nonlinearities improve neural network acoustic models","volume":"30","author":"Maas","year":"2013"},{"key":"10.1016\/j.neucom.2019.08.101_bib0041","series-title":"Proceedings of the International Conference on Computer Vision (ICCV), Pp. 1026\u20131034","article-title":"Delving deep into rectifiers: surpassing human-level performance on imagenet classification","author":"He","year":"2015"},{"key":"10.1016\/j.neucom.2019.08.101_bib0042","series-title":"Proceedings of the International Conference on Machine Learning (ICML) Workshop","article-title":"Empirical evaluation of rectified activations in convolutional network","author":"Xu","year":"2015"},{"key":"10.1016\/j.neucom.2019.08.101_bib0043","series-title":"Proceedings of the International Conference on Learning Representations (ICLR)","article-title":"Fast and accurate deep network learning by exponential linear units (elus)","author":"Clevert","year":"2016"},{"issue":"8","key":"10.1016\/j.neucom.2019.08.101_bib0044","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"10.1016\/j.neucom.2019.08.101_bib0045","series-title":"Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 2377\u20132385","article-title":"Training very deep networks","author":"Srivastava","year":"2015"},{"key":"10.1016\/j.neucom.2019.08.101_bib0046","series-title":"J. Mach. Learn. Res. (JMLR), pp. 448\u2013456","article-title":"Batch normalization: accelerating deep network training by reducing internal covariate shift","author":"Ioffe","year":"2015"},{"key":"10.1016\/j.neucom.2019.08.101_bib0047","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.neunet.2012.06.001","article-title":"Maxi-min discriminant analysis via online learning","volume":"34","author":"Xu","year":"2012","journal-title":"Neural Netw."},{"key":"10.1016\/j.neucom.2019.08.101_bib0048","unstructured":"A. Krizhevsky, G. Hinton, Learning multiple layers of features from tiny images, 2009, Tech report."},{"key":"10.1016\/j.neucom.2019.08.101_bib0049","series-title":"In: Proceedings of the 22nd ACM International Conference on Multimedia, Pp. 675\u2013678","article-title":"Caffe: convolutional architecture for fast feature embedding","author":"Jia","year":"2014"},{"key":"10.1016\/j.neucom.2019.08.101_bib0050","series-title":"ICLR","article-title":"Network in network","author":"Lin","year":"2014"},{"key":"10.1016\/j.neucom.2019.08.101_bib0051","series-title":"ICML","article-title":"Maxout networks","author":"Goodfellow","year":"2013"},{"key":"10.1016\/j.neucom.2019.08.101_bib0052","series-title":"CoRR","article-title":"Learnable pooling regions for image classification","author":"Malinowski","year":"2013"},{"key":"10.1016\/j.neucom.2019.08.101_bib0053","series-title":"ICLR","article-title":"Stochastic pooling for regularization of deep convolutional neural networks","author":"Zeiler","year":"2013"},{"key":"10.1016\/j.neucom.2019.08.101_bib0054","series-title":"NIPS","article-title":"Practical Bayesian optimization of machine learning algorithm","author":"Snock","year":"2012"},{"key":"10.1016\/j.neucom.2019.08.101_bib0055","series-title":"ICLR","article-title":"Improving deep neural networks with probabilistic maxout units","author":"Springenberg","year":"2014"},{"key":"10.1016\/j.neucom.2019.08.101_bib0056","series-title":"In NIPS","article-title":"Discriminative transfer learning with treebased priors","author":"Srivastava","year":"2013"},{"key":"10.1016\/j.neucom.2019.08.101_bib0057","series-title":"NIPS","article-title":"Deeply-supervised nets","author":"Lee","year":"2014"},{"key":"10.1016\/j.neucom.2019.08.101_bib0058","first-page":"2579","article-title":"Visualizing data using t-sne","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res. (JMLR)"},{"issue":"6","key":"10.1016\/j.neucom.2019.08.101_bib59","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1007\/s12559-018-9566-9","article-title":"A Novel Deep Density Model for Unsupervised Learning","volume":"11","author":"Yang","year":"2019","journal-title":"Cogn. Comput."},{"issue":"1","key":"10.1016\/j.neucom.2019.08.101_bib60","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s12559-017-9507-z","article-title":"Learning with memory networks from fewer samples","volume":"10","author":"Zhang","year":"2018","journal-title":"Cogn. Comput."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231220303313?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231220303313?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T06:28:23Z","timestamp":1666074503000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231220303313"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9]]},"references-count":60,"alternative-id":["S0925231220303313"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2019.08.101","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2020,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Improving deep neural network performance by integrating kernelized Min-Max objective","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2019.08.101","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}