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],
Deroski, S.[Sao],
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
Montańes, 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],
Deroski, S.[Sao],
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],
Deroski, S.[Sao],
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, T.,
Tao, D.,
Song, M.,
Maybank, S.J.,
Algorithm-Dependent Generalization Bounds for Multi-Task Learning,
PAMI(39), No. 2, February 2017, pp. 227-241.
IEEE DOI
1702
Algorithm design and analysis
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],
Deroski, S.[Sao],
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.],
Larrańaga, 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
BibRef
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
BibRef
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
BibRef
And:
Corrigendum:
PRL(112), 2018, pp. 152.
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
BibRef
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
Hong, D.F.[Dan-Feng],
Yokoya, N.[Naoto],
Xu, J.[Jian],
Zhu, X.X.[Xiao-Xiang],
Joint and Progressive Learning from High-Dimensional Data for
Multi-label Classification,
ECCV18(VIII: 478-493).
Springer DOI
1810
BibRef
Raimundo, M.M.[Marcos M.],
Von Zuben, F.J.[Fernando J.],
Many-Objective Ensemble-Based Multilabel Classification,
CIARP17(365-373).
Springer DOI
1802
BibRef
Kuga, R.[Ryohei],
Kanezaki, A.[Asako],
Samejima, M.[Masaki],
Sugano, Y.[Yusuke],
Matsushita, Y.[Yasuyuki],
Multi-task Learning Using Multi-modal Encoder-Decoder Networks with
Shared Skip Connections,
MSF17(403-411)
IEEE DOI
1802
Decoding, Feature extraction, Image segmentation, Neural networks,
Semantics, Training
BibRef
Wang, Z.,
Chen, T.,
Li, G.,
Xu, R.,
Lin, L.,
Multi-label Image Recognition by Recurrently Discovering Attentional
Regions,
ICCV17(464-472)
IEEE DOI
1802
feature extraction, image recognition,
learning (artificial intelligence), LSTM sub-network, VOC,
Semantics
BibRef
Xie, P.,
Salakhutdinov, R.,
Mou, L.,
Xing, E.P.,
Deep Determinantal Point Process for Large-Scale Multi-label
Classification,
ICCV17(473-482)
IEEE DOI
1802
image classification, learning (artificial intelligence),
neural nets, Deep Determinantal Point Process model, MLC,
Visualization
BibRef
Ghosh, A.[Aritra],
Sekhar, C.C.[C. Chandra],
Label Correlation Propagation for Semi-supervised Multi-label Learning,
PReMI17(52-60).
Springer DOI
1711
BibRef
Singh, A.K.[Abhiram Kumar],
Sekhar, C.C.[C. Chandra],
A Two-Stage Conditional Random Field Model Based Framework for
Multi-Label Classification,
PReMI17(69-76).
Springer DOI
1711
BibRef
Yang, H.,
Zhou, J.T.,
Cai, J.,
Ong, Y.S.,
MIML-FCN+: Multi-Instance Multi-Label Learning via Fully
Convolutional Networks with Privileged Information,
CVPR17(5996-6004)
IEEE DOI
1711
Correlation, Image recognition, Machine learning,
Proposals, Training
BibRef
Zhu, F.,
Li, H.,
Ouyang, W.,
Yu, N.,
Wang, X.,
Learning Spatial Regularization with Image-Level Supervisions for
Multi-label Image Classification,
CVPR17(2027-2036)
IEEE DOI
1711
Kernel, Neural networks, Semantics, Training, Visualization
BibRef
Li, Y.,
Song, Y.,
Luo, J.,
Improving Pairwise Ranking for Multi-label Image Classification,
CVPR17(1837-1845)
IEEE DOI
1711
Adaptation models, Fasteners, Neural networks, Visualization
BibRef
Yao, Y.,
Xin, X.,
Guo, P.,
A rank minimization-based late fusion method for multi-label image
annotation,
ICPR16(847-852)
IEEE DOI
1705
Matrix decomposition, Minimization, Optimization,
Predictive models, Sparse matrices, Training
BibRef
Kimura, K.,
Kudo, M.,
Sun, L.[Lu],
Koujaku, S.,
Fast random k-labELsets for large-scale multi-label classification,
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],
Multi-label learning with globAl densiTy fusiOn Mapping features,
ICPR16(462-467)
IEEE DOI
1705
Algorithm design and analysis, Clustering algorithms,
Image reconstruction, Prediction algorithms, Space exploration,
Training, Uniform, resource, locators
BibRef
Sun, L.,
Kudo, M.,
Kimura, K.,
Multi-label classification with meta-label-specific features,
ICPR16(1612-1617)
IEEE DOI
1705
Complexity theory, Correlation, Feature extraction,
Prediction algorithms, Semantics, Sparse matrices, Text, categorization
BibRef
Norov-Erdene, B.,
Kudo, M.,
Sun, L.,
Kimura, K.,
Locality in multi-label classification problems,
ICPR16(2319-2324)
IEEE DOI
1705
Biology, Clustering algorithms, Entropy, Pattern recognition,
Regression tree analysis, Testing, Training
BibRef
Kanehira, A.,
Shin, A.,
Harada, T.,
True-negative label selection for large-scale multi-label learning,
ICPR16(3673-3678)
IEEE DOI
1705
Computational modeling, Computer vision, Convergence, Dogs,
Fasteners, Optimization, Training
BibRef
Li, C.H.[Cheng-Hua],
Kang, Q.[Qi],
Ge, G.J.[Guo-Jing],
Song, Q.[Qiang],
Lu, H.Q.[Han-Qing],
Cheng, J.[Jian],
DeepBE: Learning Deep Binary Encoding for Multi-label Classification,
ChaLearn16(744-751)
IEEE DOI
1612
BibRef
Wang, J.,
Yang, Y.,
Mao, J.,
Huang, Z.,
Huang, C.,
Xu, W.,
CNN-RNN: A Unified Framework for Multi-label Image Classification,
CVPR16(2285-2294)
IEEE DOI
1612
BibRef
Kanehira, A.,
Harada, T.,
Multi-label Ranking from Positive and Unlabeled Data,
CVPR16(5138-5146)
IEEE DOI
1612
BibRef
Yang, H.[Hao],
Zhou, J.T.[Joey Tianyi],
Cai, J.F.[Jian-Fei],
Improving Multi-label Learning with Missing Labels by Structured
Semantic Correlations,
ECCV16(I: 835-851).
Springer DOI
1611
BibRef
Kimura, K.[Keigo],
Kudo, M.[Mineichi],
Sun, L.[Lu],
Simultaneous Nonlinear Label-Instance Embedding for Multi-label
Classification,
SSSPR16(15-25).
Springer DOI
1611
BibRef
Kwok, J.T.,
Learning from High-Dimensional Data in Multitask/Multilabel
Classification,
ACPR13(16-17)
IEEE DOI
1408
learning (artificial intelligence)
BibRef
Sun, F.M.[Fu-Ming],
Xu, M.X.[Mei-Xiang],
Jiang, X.J.[Xiao-Jun],
Robust Multi-Label Image Classification with Semi-Supervised Learning
and Active Learning,
MMMod15(II: 512-523).
Springer DOI
1501
BibRef
Sechidis, K.[Konstantinos],
Nikolaou, N.[Nikolaos],
Brown, G.[Gavin],
Information Theoretic Feature Selection in Multi-label Data through
Composite Likelihood,
SSSPR14(143-152).
Springer DOI
1408
BibRef
Pillai, I.[Ignazio],
Fumera, G.[Giorgio],
Roli, F.[Fabio],
Learning of Multilabel Classifiers,
ICPR14(3452-3456)
IEEE DOI
1412
Algorithm design and analysis
BibRef
Zhang, L.F.[Ling-Feng],
Shah, S.K.[Shishir K.],
Kakadiaris, I.A.[Ioannis A.],
Hierarchical Multi-label Classification using Fully Associative
Ensemble Learning,
PR(70), No. 1, 2017, pp. 89-103.
Elsevier DOI
1706
BibRef
Earlier:
Fully Associative Ensemble Learning for Hierarchical Multi-Label
Classification,
BMVC14(xx-yy).
HTML Version.
1410
Hierarchical Multi-label
BibRef
Geng, X.[Xin],
Luo, L.[Longrun],
Multilabel Ranking with Inconsistent Rankers,
CVPR14(3742-3747)
IEEE DOI
1409
BibRef
Reyes-Pupo, O.G.[Oscar Gabriel],
Morell, C.[Carlos],
Ventura-Soto, S.[Sebastián],
ReliefF-ML: An Extension of ReliefF Algorithm to Multi-label Learning,
CIARP13(II:528-535).
Springer DOI
1311
BibRef
Cai, X.[Xiao],
Nie, F.P.[Fei-Ping],
Cai, W.D.[Wei-Dong],
Huang, H.[Heng],
New Graph Structured Sparsity Model for Multi-label Image Annotations,
ICCV13(801-808)
IEEE DOI
1403
Graph Structured Sparsity
BibRef
Kong, D.G.[De-Guang],
Ding, C.[Chris],
Huang, H.[Heng],
Zhao, H.F.[Hai-Feng],
Multi-label ReliefF and F-statistic feature selections for image
annotation,
CVPR12(2352-2359).
IEEE DOI
1208
BibRef
Pang, Y.W.[Yan-Wei],
Ma, Z.[Zhao],
Yuan, Y.[Yuan],
Li, X.L.[Xue-Long],
Wang, K.Q.[Kong-Qiao],
Multimodal learning for multi-label image classification,
ICIP11(1797-1800).
IEEE DOI
1201
BibRef
Nowak, S.[Stefanie],
Llorente, A.[Ainhoa],
Motta, E.[Enrico],
Rüger, S.[Stefan],
The effect of semantic relatedness measures on multi-label
classification evaluation,
CIVR10(303-310).
DOI Link
1007
BibRef
Bucak, S.S.[Serhat S.],
Mallapragada, P.K.[Pavan Kumar],
Jin, R.[Rong],
Jain, A.K.[Anil K.],
Efficient multi-label ranking for multi-class learning:
Application to object recognition,
ICCV09(2098-2105).
IEEE DOI
0909
Not just binary classification. Order the many possible classes.
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Classifier, Performance Evaluation, Errors, Comparisons .