Fetching the paper…
Reading the bibliography…
Neural networks have recently been proposed for multi-label classification because they are able to capture and model label dependencies in the output layer.
Nature 323(6088), 533–536 (1986)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors · 1986
Earlier work this paper cites.
Complex Systems 2(6), 625–640 (1988)
Solla, S.A., Levin, E., Fleisher, M.: Accelerated learning in layered neural networks · 1988
Earlier work this paper cites.
In: Proceedings of the 10th European Conference on Machine Learning (ECML-98). pp. 137–142 (1998)
Joachims, T.: Text categorization with support vector machines: Learning with many relevant features · 1998
Earlier work this paper cites.
Machine Learning 39(2/3), 135–168 (2000)
Schapire, R.E., Singer, Y.: Boostexter: A boosting-based system for text categorization · 2000
Earlier work this paper cites.
In: Advances in Neural Information Processing Systems 14. pp. 681–687 (2001)
Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification · 2001
Earlier work this paper cites.
Journal of Machine Learning Research 5, 361–397 (2004)
Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: A new benchmark collection for text categorization research · 2004
Earlier work this paper cites.
In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management. pp. 195–200 (2005)
Ghamrawi, N., McCallum, A.: Collective multi-label classification · 2005
Earlier work this paper cites.
SIGKDD Explorations 7(1), 36–43 (2005)
Liu, T.Y., Yang, Y., Wan, H., Zeng, H.J., Chen, Z., Ma, W.Y.: Support vector machines classification with a very large-scale taxonomy · 2005
Earlier work this paper cites.
In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 217–226 (2006)
Joachims, T.: Training linear svms in linear time · 2006
Earlier work this paper cites.
IEEE Transactions on Knowledge and Data Engineering 18, 1338–1351 (2006)
Zhang, M.L., Zhou, Z.H.: Multilabel neural networks with applications to functional genomics and text categorization · 2006
Earlier work this paper cites.
Journal of Machine Learning Research 9, 1871–1874 (2008)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: A library for large linear classification · 2008
Earlier work this paper cites.
Machine Learning 73(2), 133–153 (Jun 2008)
Fürnkranz, J., Hüllermeier, E., Loza Mencía, E., Brinker, K.: Multilabel classification via calibrated label ranking · 2008
Cited alongside, same era.
Cambridge University Press (2008)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval · 2008
Cited alongside, same era.
In: Proceedings of the 9th International Conference on Music Information Retrieval. pp. 325–330 (2008)
Trohidis, K., Tsoumakas, G., Kalliris, G., Vlahavas, I.: Multi-label classification of music into emotions · 2008
Cited alongside, same era.
In: Proceedings of the 27th International Conference on Machine Learning. pp. 279–286 (2010)
Dembczyński, K., Cheng, W., Hüllermeier, E.: Bayes optimal multilabel classification via probabilistic classifier chains · 2010
Cited alongside, same era.
In: Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, JMLR W&CP. pp. 249–256 (2010)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks · 2010
Cited alongside, same era.
Machine Learning 85(3), 333–359 (2011)
Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification · 2011
Later among the works it cites.
IEEE Transactions on Knowledge and Data Engineering 23(7), 1079–1089 (2011)
Tsoumakas, G., Katakis, I., Vlahavas, I.P.: Random k-labelsets for multilabel classification · 2011
Later among the works it cites.
In: Advances in Neural Information Processing Systems 25. pp. 197–205 (2012)
Calauzènes, C., Usunier, N., Gallinari, P.: On the (non-)existence of convex, calibrated surrogate losses for ranking · 2012
Later among the works it cites.
In: Proceedings of the 29th International Conference on Machine Learning. pp. 1319–1326 (2012)
Dembczyński, K., Kotłowski, W., Hüllermeier, E.: Consistent multilabel ranking through univariate losses · 2012
Later among the works it cites.
arXiv preprint arXiv:1207.0580 (2012)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors · 2012
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Neurocomputing 73(7-9), 1164–1176 (2010)
Loza Mencía, E., Park, S.H., Fürnkranz, J.: Efficient voting prediction for pairwise multilabel classification · 2010
Cited alongside, same era.
In: Proceedings of the 27th International Conference on Machine Learning. pp. 807–814 (2010)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines · 2010
Cited alongside, same era.
In: Proceedings of the 28th International Conference on Machine Learning. pp. 17–24 (2011)
Bi, W., Kwok, J.T.: Multi-label classification on tree-and dag-structured hierarchies · 2011
Cited alongside, same era.
Journal of Machine Learning Research 12, 2121–2159 (2011)
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization · 2011
Cited alongside, same era.
In: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, JMLR W&CP. pp. 315–323 (2011)
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks · 2011
Cited alongside, same era.
Later among the works it cites.
In: Neural Networks: tricks of the trade, pp. 9–48 (2012)
LeCun, Y., Bottou, L., Orr, G.B., Müller, K.R.: Efficient backprop · 2012
Later among the works it cites.
Machine Learning 88(1-2), 157–208 (2012)
Rubin, T.N., Chambers, A., Smyth, P., Steyvers, M.: Statistical topic models for multi-label document classification · 2012
Later among the works it cites.
Machine Learning 88(1-2), 47–68 (2012)
Yang, Y., Gopal, S.: Multilabel classification with meta-level features in a learning-to-rank framework · 2012
Later among the works it cites.
Artificial Intelligence 199–200, 22–44 (2013)
Gao, W., Zhou, Z.H.: On the consistency of multi-label learning · 2013
Closest in time.
In: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. pp. 3517–3521 (2013)
Zeiler, M.D., Ranzato, M., Monga, R., Mao, M.Z., Yang, K., Le, Q.V., Nguyen, P., Senior, A., Vanhoucke, V., Dean, J., Hinton, G.E.: On rectified linear units for speech processing · 2013
Closest in time.