14.1.4.10 Multi-Label Classification, Multilabel Classification

Zhang, M.L.[Min-Ling], Zhou, Z.H.[Zhi-Hua],
ML-KNN: A lazy learning approach to multi-label learning,
PR(40), No. 7, July 2007, pp. 2038-2048.
Elsevier DOI 0704
Machine learning; Multi-label learning; Lazy learning; K-nearest neighbor; Functional genomics; Natural scene classification; Text categorization BibRef

Park, C.H.[Cheong Hee], Lee, M.H.[Moon-Hwi],
On applying linear discriminant analysis for multi-labeled problems,
PRL(29), No. 7, 1 May 2008, pp. 878-887.
Elsevier DOI 0804
Dimension reduction; Linear discriminant analysis; Multi-labeled problems; Text categorization BibRef

Han, Y., Wu, F., Zhuang, Y.T., He, X.,
Multi-Label Transfer Learning With Sparse Representation,
CirSysVideo(20), No. 8, August 2010, pp. 1110-1121.
IEEE DOI 1008
BibRef

Diez, J., del Coz, J.J., Bahamonde, A.,
A semi-dependent decomposition approach to learn hierarchical classifiers,
PR(43), No. 11, November 2010, pp. 3795-3804.
Elsevier DOI 1008
Hierarchical classification; Multi-label learning; Structured output classification; Cost-sensitive learning; Support vector machines BibRef

Barranquero, J.[Jose], Díez, J.[Jorge], del Coz, J.J.[Juan José],
Quantification-oriented learning based on reliable classifiers,
PR(48), No. 2, 2015, pp. 591-604.
Elsevier DOI 1411
Quantification BibRef

Xu, J.H.[Jian-Hua],
Fast multi-label core vector machine,
PR(46), No. 3, March 2013, pp. 885-898.
Elsevier DOI 1212
Support vector machine; Core vector machine; Multi-label classification; Frank-Wolfe method; Linear programming; Quadratic programming BibRef

Xu, J.H.[Jian-Hua],
Multi-label core vector machine with a zero label,
PR(47), No. 7, 2014, pp. 2542-2557.
Elsevier DOI 1404
Multi-label classification BibRef

Park, S.[Sunho], Choi, S.J.[Seung-Jin],
Max-margin embedding for multi-label learning,
PRL(34), No. 3, 1 February 2013, pp. 292-298.
Elsevier DOI 1301
Multi-label learning; Label embedding; Max-margin learning; Cost-sensitive multi-label hinge loss; One versus all (OVA) BibRef

Yuan, Y.[Ying], Wu, F.[Fei], Shao, J.[Jian], Zhuang, Y.T.[Yue-Ting],
Image annotation by semi-supervised cross-domain learning with group sparsity,
JVCIR(24), No. 2, February 2013, pp. 95-102.
Elsevier DOI 1302
Cross-domain; Manifold regularization; Group sparsity; Multiple kernel learning; Multi-label; Image annotation; Semi-supervise; Discriminant analysis BibRef

Lee, J.S.[Jae-Sung], Kim, D.W.[Dae-Won],
Feature selection for multi-label classification using multivariate mutual information,
PRL(34), No. 3, 1 February 2013, pp. 349-357.
Elsevier DOI 1301
Multi-label feature selection; Multivariate feature selection; Multivariate mutual information; Label dependency BibRef

Lee, J.S.[Jae-Sung], Kim, D.W.[Dae-Won],
Fast multi-label feature selection based on information-theoretic feature ranking,
PR(48), No. 9, 2015, pp. 2761-2771.
Elsevier DOI 1506
Multi-label feature selection BibRef

Lee, J.S.[Jae-Sung], Kim, D.W.[Dae-Won],
SCLS: Multi-label feature selection based on scalable criterion for large label set,
PR(66), No. 1, 2017, pp. 342-352.
Elsevier DOI 1704
Machine learning BibRef

Lim, H., Kim, D.W.,
Convex optimization approach for multi-label feature selection based on mutual information,
ICPR16(1512-1517)
IEEE DOI 1705
Convex functions, Entropy, Linear programming, Mutual information, Optimization, Redundancy, Time, complexity BibRef

Lim, H.K.[Hyun-Ki], Lee, J.S.[Jae-Sung], Kim, D.W.[Dae-Won],
Accelerating Multi-Label Feature Selection Based on Low-Rank Approximation,
IEICE(E99-D), No. 5, May 2016, pp. 1396-1399.
WWW Link. 1605
BibRef

Lim, H.K.[Hyun-Ki], Lee, J.S.[Jae-Sung], Kim, D.W.[Dae-Won],
Optimization approach for feature selection in multi-label classification,
PRL(89), No. 1, 2017, pp. 25-30.
Elsevier DOI 1704
Multi-label feature selection BibRef

Kocev, D.[Dragi], Vens, C.[Celine], Struyf, J.[Jan], Džeroski, S.[Sašo],
Tree ensembles for predicting structured outputs,
PR(46), No. 3, March 2013, pp. 817-833.
Elsevier DOI 1212
Ensemble methods; Predictive clustering trees; Structured outputs; Multi-target regression; Multi-target classification; Hierarchical multi-label classification BibRef

Naula, P.[Pekka], Airola, A.[Antti], Salakoski, T.[Tapio], Pahikkala, T.[Tapio],
Multi-label learning under feature extraction budgets,
PRL(40), No. 1, 2014, pp. 56-65.
Elsevier DOI 1403
Feature selection BibRef

Wang, S.F.[Shang-Fei], Wang, J.[Jun], Wang, Z.Y.[Zhao-Yu], Ji, Q.A.[Qi-Ang],
Enhancing multi-label classification by modeling dependencies among labels,
PR(47), No. 10, 2014, pp. 3405-3413.
Elsevier DOI 1406
Multi-label classification BibRef

Montañes, E.[Elena], Senge, R.[Robin], Barranquero, J.[Jose], Quevedo, J.R.[José Ramón], del Coz, J.J.[Juan José], Hüllermeier, E.[Eyke],
Dependent binary relevance models for multi-label classification,
PR(47), No. 3, 2014, pp. 1494-1508.
Elsevier DOI 1312
Multi-label classification BibRef

Madjarov, G.[Gjorgji], Gjorgjevikj, D.[Dejan], Džeroski, S.[Sašo],
Two stage architecture for multi-label learning,
PR(45), No. 3, March 2012, pp. 1019-1034.
Elsevier DOI 1111
BibRef
And:
Dual Layer Voting Method for Efficient Multi-label Classification,
IbPRIA11(232-239).
Springer DOI 1106
Multi-label learning; Multi-label ranking; Multi-label classification; Two stage architecture; Classifier chain BibRef

Madjarov, G.[Gjorgji], Kocev, D.[Dragi], Gjorgjevikj, D.[Dejan], Džeroski, S.[Sašo],
An extensive experimental comparison of methods for multi-label learning,
PR(45), No. 9, September 2012, pp. 3084-3104.
Elsevier DOI 1206
Multi-label ranking; Multi-label classification; Comparison of multi-label learning methods BibRef

Luo, Y., Tao, D.C., Geng, B., Xu, C., Maybank, S.J.,
Manifold Regularized Multitask Learning for Semi-Supervised Multilabel Image Classification,
IP(22), No. 2, February 2013, pp. 523-536.
IEEE DOI 1302
BibRef

Gong, C., Tao, D., Maybank, S.J., Liu, W., Kang, G., Yang, J.,
Multi-Modal Curriculum Learning for Semi-Supervised Image Classification,
IP(25), No. 7, July 2016, pp. 3249-3260.
IEEE DOI 1606
image classification BibRef

Liu, Q., Sun, Y., Wang, C., Liu, T., Tao, D.,
Elastic Net Hypergraph Learning for Image Clustering and Semi-Supervised Classification,
IP(26), No. 1, January 2017, pp. 452-463.
IEEE DOI 1612
graph theory BibRef

Quevedo, J.R.[José Ramón], Luaces, O.[Oscar], Bahamonde, A.[Antonio],
Multilabel classifiers with a probabilistic thresholding strategy,
PR(45), No. 2, February 2012, pp. 876-883.
Elsevier DOI 1110
Multilabel classification; Thresholding strategies; Posterior probability; Expected loss BibRef

Lastra, G.[Gerardo], Luaces, O.[Oscar], Bahamonde, A.[Antonio],
Interval prediction for graded multi-label classification,
PRL(49), No. 1, 2014, pp. 171-176.
Elsevier DOI 1410
Graded multi-label classification BibRef

Dimitrovski, I.[Ivica], Kocev, D.[Dragi], Loskovska, S.[Suzana], Džeroski, S.[Sašo],
Fast and efficient visual codebook construction for multi-label annotation using predictive clustering trees,
PRL(38), No. 1, 2014, pp. 38-45.
Elsevier DOI 1402
Automatic image annotation BibRef

Sun, F.M.[Fu-Ming], Tang, J.H.[Jin-Hui], Li, H.J.[Hao-Jie], Qi, G.J.[Guo-Jun], Huang, T.S.,
Multi-Label Image Categorization With Sparse Factor Representation,
IP(23), No. 3, March 2014, pp. 1028-1037.
IEEE DOI 1403
multi-label classification reveals underlying correlations. correlation methods BibRef

Pei, Y.L.[Yuan-Li], Fern, X.Z.[Xiaoli Z.],
Constrained instance clustering in multi-instance multi-label learning,
PRL(37), No. 1, 2014, pp. 107-114.
Elsevier DOI 1402
MIML BibRef

Pham, A.T., Raich, R., Fern, X.Z.,
Dynamic Programming for Instance Annotation in Multi-Instance Multi-Label Learning,
PAMI(39), No. 12, December 2017, pp. 2381-2394.
IEEE DOI 1711
Computational modeling, Data models, Dynamic programming, Graphical models, Labeling, Probabilistic logic, Multi-instance multi-label learning, expectation maximization, instance annotation BibRef

Sucar, L.E.[L. Enrique], Bielza, C.[Concha], Morales, E.F.[Eduardo F.], Hernandez-Leal, P.[Pablo], Zaragoza, J.H.[Julio H.], Larrañaga, P.[Pedro],
Multi-label classification with Bayesian network-based chain classifiers,
PRL(41), No. 1, 2014, pp. 14-22.
Elsevier DOI 1403
Multi-label classification BibRef

Zhang, Q., Chen, L., Li, B.,
Max-Margin Multiattribute Learning With Low-Rank Constraint,
IP(23), No. 7, July 2014, pp. 2866-2876.
IEEE DOI 1407
Accuracy BibRef

Gönen, M.[Mehmet],
Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning,
PRL(38), No. 1, 2014, pp. 132-141.
Elsevier DOI 1402
Multilabel learning BibRef

Zhang, M., Wu, L.,
Lift: Multi-Label Learning with Label-Specific Features,
PAMI(37), No. 1, January 2015, pp. 107-120.
IEEE DOI 1412
Algorithm design and analysis BibRef

Kim, M.Y.[Min-Young],
Multiple-concept feature generative models for multi-label image classification,
CVIU(136), No. 1, 2015, pp. 69-78.
Elsevier DOI 1506
Probabilistic graphical models BibRef

Kim, M.Y.[Min-Young],
Sparse conditional copula models for structured output regression,
PR(60), No. 1, 2016, pp. 761-769.
Elsevier DOI 1609
Multiple output regression BibRef

Lim, H.K.[Hyun-Ki], Lee, J.S.[Jae-Sung], Kim, D.W.[Dae-Won],
Multi-Label Learning Using Mathematical Programming,
IEICE(E98-D), No. 1, January 2015, pp. 197-200.
WWW Link. 1503
BibRef

Li, X.M.[Xi-Ming], Ouyang, J.H.[Ji-Hong], Zhou, X.T.[Xiao-Tang],
Centroid prior topic model for multi-label classification,
PRL(62), No. 1, 2015, pp. 8-13.
Elsevier DOI 1507
Multi-label classification BibRef

Destercke, S.[Sébastien],
Multilabel predictions with sets of probabilities: The Hamming and ranking loss cases,
PR(48), No. 11, 2015, pp. 3757-3765.
Elsevier DOI 1506
Multilabel BibRef

Ivasic-Kos, M.[Marina], Pobar, M.[Miran], Ribaric, S.[Slobodan],
Two-tier image annotation model based on a multi-label classifier and fuzzy-knowledge representation scheme,
PR(52), No. 1, 2016, pp. 287-305.
Elsevier DOI 1601
Image annotation BibRef

Li, X., Zhao, X., Zhang, Z., Wu, F., Zhuang, Y., Wang, J., Li, X.,
Joint Multilabel Classification With Community-Aware Label Graph Learning,
IP(25), No. 1, January 2016, pp. 484-493.
IEEE DOI 1601
Computer vision BibRef

Triguero, I.[Isaac], Vens, C.[Celine],
Labelling strategies for hierarchical multi-label classification techniques,
PR(56), No. 1, 2016, pp. 170-183.
Elsevier DOI 1604
Hierarchical multi-label classification BibRef

Jing, X.Y., Wu, F., Li, Z., Hu, R., Zhang, D.,
Multi-Label Dictionary Learning for Image Annotation,
IP(25), No. 6, June 2016, pp. 2712-2725.
IEEE DOI 1605
Computational modeling BibRef

Li, Q.[Qiang], Xie, B.[Bo], You, J.[Jane], Bian, W.[Wei], Tao, D.C.[Da-Cheng],
Correlated Logistic Model With Elastic Net Regularization for Multilabel Image Classification,
IP(25), No. 8, August 2016, pp. 3801-3813.
IEEE DOI 1608
correlation methods BibRef

Pu, J.M.[Jia-Meng], Zhang, Q., Zhang, L., Du, B., You, J.[Jane],
Multiview clustering based on Robust and Regularized Matrix Approximation,
ICPR16(2550-2555)
IEEE DOI 1705
Approximation algorithms, Clustering algorithms, Linear programming, Manifolds, Matrix decomposition, Optimization, Robustness BibRef

Li, Q.[Qiang], Qiao, M.Y.[Mao-Ying], Bian, W.[Wei], Tao, D.C.[Da-Cheng],
Conditional Graphical Lasso for Multi-label Image Classification,
CVPR16(2977-2986)
IEEE DOI 1612
BibRef

Liu, B.[Bin], Xu, Z.L.[Zeng-Lin], Wu, S.[Shuang], Wang, F.[Fei],
Manifold regularized matrix completion for multilabel classification,
PRL(80), No. 1, 2016, pp. 58-63.
Elsevier DOI 1609
Multi-label learning BibRef

Pillai, I.[Ignazio], Fumera, G.[Giorgio], Roli, F.[Fabio],
Designing multi-label classifiers that maximize F measures: State of the art,
PR(61), No. 1, 2017, pp. 394-404.
Elsevier DOI 1705
Multi-label classification BibRef

Read, J.[Jesse], Martino, L.[Luca], Hollmén, J.[Jaakko],
Multi-label methods for prediction with sequential data,
PR(63), No. 1, 2017, pp. 45-55.
Elsevier DOI 1612
Multi-label classification BibRef

Wang, M., Luo, C., Hong, R., Tang, J., Feng, J.,
Beyond Object Proposals: Random Crop Pooling for Multi-Label Image Recognition,
IP(25), No. 12, December 2016, pp. 5678-5688.
IEEE DOI 1612
image recognition BibRef

Xia, Y., Nie, L., Zhang, L., Yang, Y., Hong, R., Li, X.,
Weakly Supervised Multilabel Clustering and its Applications in Computer Vision,
Cyber(46), No. 12, December 2016, pp. 3220-3232.
IEEE DOI 1612
Clustering algorithms BibRef

Du, B.[Bo], Wang, Z., Zhang, L.F.[Le-Fei], Zhang, L.P.[Liang-Pei], Tao, D.C.[Da-Cheng],
Robust and Discriminative Labeling for Multi-Label Active Learning Based on Maximum Correntropy Criterion,
IP(26), No. 4, April 2017, pp. 1694-1707.
IEEE DOI 1704
data mining BibRef

Wang, Z.M.[Zeng-Mao], Du, B.[Bo], Zhang, L.F.[Le-Fei], Zhang, L.P.[Liang-Pei], Fang, M.[Meng], Tao, D.C.[Da-Cheng],
Multi-label Active Learning Based on Maximum Correntropy Criterion: Towards Robust and Discriminative Labeling,
ECCV16(III: 453-468).
Springer DOI 1611
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Akbarnejad, A.[Amirhossein], Baghshah, M.S.[Mahdieh Soleymani],
A probabilistic multi-label classifier with missing and noisy labels handling capability,
PRL(89), No. 1, 2017, pp. 18-24.
Elsevier DOI 1704
Multi-label classification BibRef

Wu, J.[Jian], Ye, C.[Chen], Sheng, V.S.[Victor S.], Zhang, J.[Jing], Zhao, P.P.[Peng-Peng], Cui, Z.M.[Zhi-Ming],
Active learning with label correlation exploration for multi-label image classification,
IET-CV(11), No. 7, October 2017, pp. 577-584.
DOI Link 1709
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Earlier: A1, A3, A4, A5, A6, Only:
Multi-label active learning for image classification,
ICIP14(5227-5231)
IEEE DOI 1502
BibRef
And: A2, A1, A3, A5, A6, Only:
Multi-label active learning with label correlation for image classification,
ICIP15(3437-3441)
IEEE DOI 1512
Accuracy. Multi-label BibRef

Barezi, E.J.[Elham J.], Kwok, J.T.[James T.], Rabiee, H.R.[Hamid R.],
Multi-Label learning in the independent label sub-spaces,
PRL(97), No. 1, 2017, pp. 8-12.
Elsevier DOI 1709
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Elsevier DOI 1809
Large scale learning BibRef

Sun, L.[Lu], Kudo, M.[Mineichi],
Optimization of classifier chains via conditional likelihood maximization,
PR(74), No. 1, 2018, pp. 503-517.
Elsevier DOI 1711
Multi-label classification BibRef

Trajdos, P.[Pawel], Kurzynski, M.[Marek],
Weighting scheme for a pairwise multi-label classifier based on the fuzzy confusion matrix,
PRL(103), 2018, pp. 60-67.
Elsevier DOI 1802
Multi-label classification, Label pairwise transformation, Random reference classifier, Confusion matrix, Entropy BibRef

Zhang, H.[Hu], Wu, W.[Wei], Wang, D.[Ding],
Multi-instance multi-label learning of natural scene images: via sparse coding and multi-layer neural network,
IET-CV(12), No. 3, April 2018, pp. 305-311.
DOI Link 1804
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Wang, S.F.[Shang-Fei], Chen, S.[Shiyu], Chen, T.F.[Tan-Fang], Shi, X.X.[Xiao-Xiao],
Learning with privileged information for multi-Label classification,
PR(81), 2018, pp. 60-70.
Elsevier DOI 1806
Privileged information, Multi-label classification, Similarity constraints BibRef

Ahmadi, Z.[Zahra], Kramer, S.[Stefan],
A label compression method for online multi-label classification,
PRL(111), 2018, pp. 64-71.
Elsevier DOI 1808
Data stream classification, Multi-label data, Label compression BibRef

Zhang, J., Wu, Q., Shen, C., Zhang, J., Lu, J.,
Multilabel Image Classification With Regional Latent Semantic Dependencies,
MultMed(20), No. 10, October 2018, pp. 2801-2813.
IEEE DOI 1810
image classification, learning (artificial intelligence), recurrent neural nets, multilabel image classification, deep neural network BibRef

Jin, C.[Cong], Jin, S.W.[Shu-Wei],
Multi-label automatic image annotation approach based on multiple improvement strategies,
IET-IPR(13), No. 4, March 2019, pp. 623-633.
DOI Link 1903
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Kumar, V.[Vikas], Pujari, A.K.[Arun K], Padmanabhan, V.[Vineet], Kagita, V.R.[Venkateswara Rao],
Group preserving label embedding for multi-label classification,
PR(90), 2019, pp. 23-34.
Elsevier DOI 1903
Multi-label classification, Label embedding, Matrix factorization BibRef

Nguyen, T.T.[Tien Thanh], Nguyen, T.T.T.[Thi Thu Thuy], Luong, A.V.[Anh Vu], Nguyen, Q.V.H.[Quoc Viet Hung], Liew, A.W.C.[Alan Wee-Chung], Stantic, B.[Bela],
Multi-label classification via label correlation and first order feature dependance in a data stream,
PR(90), 2019, pp. 35-51.
Elsevier DOI 1903
Multi-label classification, Multi-label learning, Online learning, Data stream, Concept drift, Label correlation, Feature dependence BibRef

Yu, W.J.[Wan-Jin], Chen, Z.D.[Zhen-Duo], Luo, X.[Xin], Liu, W.[Wu], Xu, X.S.[Xin-Shun],
DELTA: A deep dual-stream network for multi-label image classification,
PR(91), 2019, pp. 322-331.
Elsevier DOI 1904
Deep neural network, Dual-stream network, Multi-label image classification, Multi-instance learning BibRef

Zhang, J.[Juli], Zhang, J.[Junyi], Dai, T.[Tao], He, Z.Z.[Zhan-Zhuang],
Exploring Weighted Dual Graph Regularized Non-Negative Matrix Tri-Factorization Based Collaborative Filtering Framework for Multi-Label Annotation of Remote Sensing Images,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link 1905
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Lyu, F.[Fan], Wu, Q.[Qi], Hu, F.Y.[Fu-Yuan], Wu, Q.Y.[Qing-Yao], Tan, M.K.[Ming-Kui],
Attend and Imagine: Multi-Label Image Classification With Visual Attention and Recurrent Neural Networks,
MultMed(21), No. 8, August 2019, pp. 1971-1981.
IEEE DOI 1908
image classification, recurrent neural nets, multilabel image classification, recurrent neural networks, Recurrent Neural Network (RNN) BibRef

Chen, L.[Long], Wang, R.G.[Rong-Gui], Yang, J.[Juan], Xue, L.X.[Li-Xia], Hu, M.[Min],
Multi-label image classification with recurrently learning semantic dependencies,
VC(35), No. 10, October 2018, pp. 1361-1371.
WWW Link. 1909
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Xue, L.X.[Li-Xia], Jiang, D.[Di], Wang, R.G.[Rong-Gui], Yang, J.[Juan], Hu, M.[Min],
Learning semantic dependencies with channel correlation for multi-label classification,
VC(36), No. 7, July 2020, pp. 1325-1335.
WWW Link. 2005
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Huang, S.J.[Sheng-Jun], Gao, W.[Wei], Zhou1, Z.H.[Zhi-Hua],
Fast Multi-Instance Multi-Label Learning,
PAMI(41), No. 11, November 2019, pp. 2614-2627.
IEEE DOI 1910
Task analysis, Computational modeling, Semantics, Predictive models, Prediction algorithms, sub-concepts BibRef

Zhang, Y.[Yu], Wang, Y.[Yin], Liu, X.Y.[Xu-Ying], Mi, S.[Siya], Zhang, M.L.[Min-Ling],
Large-scale multi-label classification using unknown streaming images,
PR(99), 2020, pp. 107100.
Elsevier DOI 1912
Multi-label image classification, Recurrent novel-class detector, Streaming images BibRef

Vanegas, J.A.[Jorge A.], Beltrán, V.[Viviana], Escalante, H.J.[Hugo Jair], González, F.A.[Fabio A.],
Transductive non-linear semantic embedding for multi-class classification,
PRL(128), 2019, pp. 370-377.
Elsevier DOI 1912
Semantic representation, Semi-supervised learning, Transductive learning, Learning on a budget, Multi-class classification BibRef

Dai, Y.[Yong], Li, Y.[Yi], Li, S.T.[Shu-Tao],
Multi-label learning for concept-oriented labels of product image data,
IVC(93), 2020, pp. 103821.
Elsevier DOI 2001
Multi-label learning, Concept-oriented labels, Product image data, Feature correlation learning, Gram matrices BibRef

Xia, Y.X.[Yuan-Xin], d'Angelo, P.[Pablo], Tian, J.J.[Jiao-Jiao], Fraundorfer, F.[Friedrich], Reinartz, P.[Peter],
Multi-Label Learning based Semi-Global Matching Forest,
RS(12), No. 7, 2020, pp. xx-yy.
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Yeh, M.C.[Mei-Chen], Li, Y.N.[Yi-Nan],
Multilabel Deep Visual-Semantic Embedding,
PAMI(42), No. 6, June 2020, pp. 1530-1536.
IEEE DOI 2005
Semantics, Computational modeling, Visualization, Training, Task analysis, Convolutional neural networks, Redundancy, convolutional neural networks BibRef

Hu, L.[Liang], Li, Y.[Yonghao], Gao, W.[Wanfu], Zhang, P.[Ping], Hu, J.[Juncheng],
Multi-label feature selection with shared common mode,
PR(104), 2020, pp. 107344.
Elsevier DOI 2005
Feature selection, Multi-label learning, Coupled matrix factorization, Classification BibRef

Cevikalp, H.[Hakan], Benligiray, B.[Burak], Gerek, O.N.[Omer Nezih],
Semi-supervised robust deep neural networks for multi-label image classification,
PR(100), 2020, pp. 107164.
Elsevier DOI 2005
Multi-label classification, Semi-supervised learning, Ramp loss, Image classification, Deep learning BibRef

Zhu, P.P.[Pan-Pan], Tan, Y.M.[Yu-Min], Zhang, L.Q.[Li-Qiang], Wang, Y.B.[Yue-Bin], Mei, J.[Jie], Liu, H.[Hao], Wu, M.F.[Meng-Fan],
Deep Learning for Multilabel Remote Sensing Image Annotation With Dual-Level Semantic Concepts,
GeoRS(58), No. 6, June 2020, pp. 4047-4060.
IEEE DOI 2005
Attention mechanism, dual-level semantic concepts, remote sensing (RS) image multilabel annotation, triplet loss BibRef

Hua, Y.S.[Yuan-Sheng], Mou, L.C.[Li-Chao], Zhu, X.X.[Xiao Xiang],
Relation Network for Multilabel Aerial Image Classification,
GeoRS(58), No. 7, July 2020, pp. 4558-4572.
IEEE DOI 2006
Feature extraction, Semantics, Cognition, Correlation, Remote sensing, Task analysis, Soil, Attentional region extraction, multilabel classification BibRef

Rajat, Varshney, M., Singh, P., Namboodiri, V.P.,
Minimizing Supervision in Multi-label Categorization,
TCV20(93-102)
IEEE DOI 2008
Measurement, Task analysis, Machine learning, Welding, Feature extraction, Uncertainty, Computational modeling BibRef

Huynh, D., Elhamifar, E.,
Interactive Multi-Label CNN Learning With Partial Labels,
CVPR20(9420-9429)
IEEE DOI 2008
Training, Loss measurement, Training data, Manifolds, Memory management, Image recognition, Standards BibRef

Guan, Z.Q.[Zi-Qiao], Yager, K.G.[Kevin G.], Yu, D.T.[Dan-Tong], Qin, H.[Hong],
Multi-Label Visual Feature Learning with Attentional Aggregation,
WACV20(2190-2198)
IEEE DOI 2006
identifying weak scattered patterns with diffuse background interference. X-ray scattering, Active appearance model, Visualization, Imaging, Training, X-ray imaging, Detectors BibRef

Trajdos, P.[Pawel], Kurzynski, M.[Marek],
Dynamic Classifier Chains for Multi-label Learning,
GCPR19(567-580).
Springer DOI 1911
BibRef

Wang, Q., Jia, N., Breckon, T.P.,
A Baseline for Multi-Label Image Classification Using an Ensemble of Deep Convolutional Neural Networks,
ICIP19(644-648)
IEEE DOI 1910
Multi-Label Image Classification, Deep Convolutional Neural Network, Data Augmentation BibRef

Chen, Z., Lin, J., Wang, Z., Chandrasekhar, V., Lin, W.,
Beyond Ranking Loss: Deep Holographic Networks for Multi-Label Video Search,
ICIP19(879-883)
IEEE DOI 1910
Video Search, Multi-label, Deep Metric Learning, Feature Composition BibRef

Zhang, M.Y.[Meng-Ying], Li, C.S.[Chang-Sheng], Wang, X.F.[Xiang-Feng],
Multi-View Metric Learning for Multi-Label Image Classification,
ICIP19(2134-2138)
IEEE DOI 1910
Multi-label image classification, distance metric learning, multi-view learning BibRef

Jiang, B., Zhou, L., Lin, L., Xu, B., Yu, J., Zheng, X., Wu, K.,
A Real-Time Multi-Label Classification System for Short Videos,
ICIP19(534-538)
IEEE DOI 1910
Short-video analysis, multi-label classification, deep learning, real-time inference BibRef

Guo, B., Hou, C., Shan, J., Yi, D.,
Low Rank Multi-Label Classification with Missing Labels,
ICPR18(417-422)
IEEE DOI 1812
Correlation, Optimization, Semantics, Linear programming, Matrices, Data models, Training BibRef

Pei, Y., Fern, X., Raich, R.,
Learning with Latent Label Hierarchy from Incomplete Multi-Label Data,
ICPR18(2075-2080)
IEEE DOI 1812
Labeling, Graphical models, Maximum likelihood estimation, Message passing, Probabilistic logic, Training, Dynamic programming BibRef

Firman, M.[Michael], Campbell, N.D.F.[Neill D. F.], Agapito, L.[Lourdes], Brostow, G.J.[Gabriel J.],
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CVPR17(5996-6004)
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CVPR17(2027-2036)
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Kernel, Neural networks, Semantics, Training, Visualization BibRef

Li, Y., Song, Y., Luo, J.,
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CVPR17(1837-1845)
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ICPR16(847-852)
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Kimura, K., Kudo, M., Sun, L.[Lu], Koujaku, S.,
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ICPR16(438-443)
IEEE DOI 1705
Algorithm design and analysis, Complexity theory, Computational modeling, Support vector machines, Testing, Training BibRef

Guo, Y.[Yumeng], Chung, F.[Fulai], Li, G.[Guozheng],
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Algorithm design and analysis, Clustering algorithms, Image reconstruction, Prediction algorithms, Space exploration, Training, Uniform, resource, locators BibRef

Sun, L., Kudo, M., Kimura, K.,
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ICPR16(1612-1617)
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ICPR16(2319-2324)
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Biology, Clustering algorithms, Entropy, Pattern recognition, Regression tree analysis, Testing, Training BibRef

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ICPR16(3673-3678)
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Computational modeling, Computer vision, Convergence, Dogs, Fasteners, Optimization, Training BibRef

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Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Classifier, Performance Evaluation, Errors, Comparisons .