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ZHANG M L, ZHOU Z H. A review on multi-label learning algorithms[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(8): 1819-1837.
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READ J, PFAHRINGER B, HOLMES G, et al. Classifier chains for multi-label classification[J]. Machine Learning, 2011, 85(3): 333-359.
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ZAHARIA M, CHOWDHURY M, DAS T, et al. Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing[C]// Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation. San Hose, USA: USENIX Association, 2012: 141-146.
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ZHU B, MARA A, MOZO A. CLUS: Parallel subspace clustering algorithm on spark[A]// New Trends in Databases and Information Systems[M]. Springer, 2015, 539:175-185.
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MAILLO J, RAMREZ S, TRIGUERO I, et al. kNN-IS: An iterative spark-based design of the k-nearest neighbors classifier for big data[J]. Knowledge-Based Systems, 2016, 117: 3-15; doi: 10.1016/j.knosys.2016.06.012.
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KIM H, PARK J, JANG J, et al. DeepSpark: Spark-based deep learning supporting asynchronous updates and Caffe compatibility[J/OL]. https://arxiv.org/abs/1602.08191v1,2016.03.08, 2016: arXiv:1602.08191v1.
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DUAN M X, LI K L, TANG Z, et al. Selection and replacement algorithms for memory performance improvement in Spark[J]. Concurrency & Computation Practice & Experience, 2015, 28(8): 2473-2486.
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TSOUMAKAS G, KATAKIS I, VLAHAVAS I. Mining multi-label data[A]// Data Mining and Knowledge Discovery Handbook[M]. Springer, 2009: 667-685.
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READ J, PFAHRINGER B, HOLMES G. Multi-label classification using ensembles of pruned sets[C]// Proceedings of the 8th International Conference on Data Mining. Pisa, Italy: IEEE Computer Society, 2008: 995-1000.
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TSOUMAKAS G, SPYROMITROS-XIOUFIS E, VILCEK J, et al. Mulan: A Java library for multi-label learning[J]. Journal of Machine Learning Research, 2011, 12(2): 2411-2414.
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MENCA E L, FRNKRANZ J. Efficient pairwise multilabel classification for large-scale problems in the legal domain[C]// European Conference on Machine Learning & Knowledge Discovery in Databases. Antwerp, Belgium: Springer, 2008: 50-65.
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SNOEK C G M, WORRING M, VAN GEMERT J C, et al. The challenge problem for automated detection of 101 semantic concepts in multimedia[C]// Proceedings of the 14th ACM International Conference on Multimedia. Santa Barbara, USA: ACM Press. 2006: 421-430.
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CHUA T S, TANG J, HONG R, et al. NUS-WIDE: A real-world web image database from National University of Singapore[C]// Proceedings of the ACM International Conference on Image and Video Retrieval. Santorini, Greece: ACM Press, 2009: doi: 10.1145/1646396.1646452.
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SPYROMITROS-XIOUFIS E, PAPADOPOULOS S, KOMPATSIARIS I Y, et al. A comprehensive study over VLAD and product quantization in large-scale image retrieval[J]. IEEE Transactions on Multimedia, 2014, 16(6): 1713-1728.
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ZHANG M L, WU L. LIFT: Multi-label learning with label-specific features[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 37(1): 107-20.
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[16] |
FRNKRANZ J, HLLERMEIER E, MENCA E L, et al. Multilabel classification via calibrated label ranking[J]. Machine Learning, 2008, 73(2): 133-153.
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[17] |
TSOUMAKAS G, KATAKIS I, VLAHAVAS I. Random k-labelsets for multilabel classification[J]. IEEE Transactions on Knowledge & Data Engineering, 2011, 23(7): 1079-1089.
|
[1] |
ZHANG M L, ZHOU Z H. A review on multi-label learning algorithms[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(8): 1819-1837.
|
[2] |
READ J, PFAHRINGER B, HOLMES G, et al. Classifier chains for multi-label classification[J]. Machine Learning, 2011, 85(3): 333-359.
|
[3] |
ZAHARIA M, CHOWDHURY M, DAS T, et al. Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing[C]// Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation. San Hose, USA: USENIX Association, 2012: 141-146.
|
[4] |
ZHU B, MARA A, MOZO A. CLUS: Parallel subspace clustering algorithm on spark[A]// New Trends in Databases and Information Systems[M]. Springer, 2015, 539:175-185.
|
[5] |
MAILLO J, RAMREZ S, TRIGUERO I, et al. kNN-IS: An iterative spark-based design of the k-nearest neighbors classifier for big data[J]. Knowledge-Based Systems, 2016, 117: 3-15; doi: 10.1016/j.knosys.2016.06.012.
|
[6] |
KIM H, PARK J, JANG J, et al. DeepSpark: Spark-based deep learning supporting asynchronous updates and Caffe compatibility[J/OL]. https://arxiv.org/abs/1602.08191v1,2016.03.08, 2016: arXiv:1602.08191v1.
|
[7] |
DUAN M X, LI K L, TANG Z, et al. Selection and replacement algorithms for memory performance improvement in Spark[J]. Concurrency & Computation Practice & Experience, 2015, 28(8): 2473-2486.
|
[8] |
TSOUMAKAS G, KATAKIS I, VLAHAVAS I. Mining multi-label data[A]// Data Mining and Knowledge Discovery Handbook[M]. Springer, 2009: 667-685.
|
[9] |
READ J, PFAHRINGER B, HOLMES G. Multi-label classification using ensembles of pruned sets[C]// Proceedings of the 8th International Conference on Data Mining. Pisa, Italy: IEEE Computer Society, 2008: 995-1000.
|
[10] |
TSOUMAKAS G, SPYROMITROS-XIOUFIS E, VILCEK J, et al. Mulan: A Java library for multi-label learning[J]. Journal of Machine Learning Research, 2011, 12(2): 2411-2414.
|
[11] |
MENCA E L, FRNKRANZ J. Efficient pairwise multilabel classification for large-scale problems in the legal domain[C]// European Conference on Machine Learning & Knowledge Discovery in Databases. Antwerp, Belgium: Springer, 2008: 50-65.
|
[12] |
SNOEK C G M, WORRING M, VAN GEMERT J C, et al. The challenge problem for automated detection of 101 semantic concepts in multimedia[C]// Proceedings of the 14th ACM International Conference on Multimedia. Santa Barbara, USA: ACM Press. 2006: 421-430.
|
[13] |
CHUA T S, TANG J, HONG R, et al. NUS-WIDE: A real-world web image database from National University of Singapore[C]// Proceedings of the ACM International Conference on Image and Video Retrieval. Santorini, Greece: ACM Press, 2009: doi: 10.1145/1646396.1646452.
|
[14] |
SPYROMITROS-XIOUFIS E, PAPADOPOULOS S, KOMPATSIARIS I Y, et al. A comprehensive study over VLAD and product quantization in large-scale image retrieval[J]. IEEE Transactions on Multimedia, 2014, 16(6): 1713-1728.
|
[15] |
ZHANG M L, WU L. LIFT: Multi-label learning with label-specific features[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 37(1): 107-20.
|
[16] |
FRNKRANZ J, HLLERMEIER E, MENCA E L, et al. Multilabel classification via calibrated label ranking[J]. Machine Learning, 2008, 73(2): 133-153.
|
[17] |
TSOUMAKAS G, KATAKIS I, VLAHAVAS I. Random k-labelsets for multilabel classification[J]. IEEE Transactions on Knowledge & Data Engineering, 2011, 23(7): 1079-1089.
|