A stream-based semi-supervised active learning approach for document classification. ICDAR, pp.611-615, 2013. ,
URL : https://hal.archives-ouvertes.fr/hal-00855184
Identifying mislabeled training data, Journal of Artificial Intelligence Research, pp.131-167, 1999. ,
Coarse sample complexity bounds for active learning, Neural Information Processing Systems (NIPS), pp.235-242, 2005. ,
Active learning with uncertain labeling knowledge, Pattern Recognition Letters, vol.43, pp.98-108, 2013. ,
DOI : 10.1016/j.patrec.2013.10.011
Classification in the Presence of Label Noise: A Survey, IEEE Transactions on Neural Networks and Learning Systems, vol.25, issue.5, pp.845-869, 2013. ,
DOI : 10.1109/TNNLS.2013.2292894
Noise elimination in inductive concept learning: A case study in medical diagnosis, Algorithmic Learning Theory, pp.199-212, 1996. ,
DOI : 10.1007/3-540-61863-5_47
Oasis: Online active semi-supervised learning, AAAI Conference on Artificial Intelligence, pp.1-6, 2011. ,
Graph-based active semi-supervised learning: A new perspective for relieving multi-class annotation labor, 2014 IEEE International Conference on Multimedia and Expo (ICME), pp.1-6, 2014. ,
DOI : 10.1109/ICME.2014.6890274
Repeated labeling using multiple noisy labelers, ACM Conference on Knowledge Discovery and Data Mining, pp.402-441, 2014. ,
DOI : 10.1007/s10618-013-0306-1
URL : http://archive.nyu.edu/bitstream/2451/29799/2/CeDER-10-03.pdf
Active learning with support vector machines, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol.20, issue.4, pp.313-326, 2014. ,
DOI : 10.1002/widm.1132
Active-transductive learning with labeladapted kernels, ACM SIGKDD international conference on Knowledge discovery and data mining, pp.462-471, 2014. ,
DOI : 10.1145/2623330.2623673
scikit-learn, scikit-learn: Machine learning in Python, Journal of Machine Learning Research, pp.2825-2830, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00650905
Active Label Correction, 2012 IEEE 12th International Conference on Data Mining, pp.1080-1085, 2012. ,
DOI : 10.1109/ICDM.2012.162
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics, vol.20, pp.53-65, 1987. ,
DOI : 10.1016/0377-0427(87)90125-7
Active Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning, vol.6, issue.1, pp.1-114, 2012. ,
DOI : 10.2200/S00429ED1V01Y201207AIM018
Get another label? improving data quality and data mining using multiple, noisy labelers, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 08, pp.614-622, 2008. ,
DOI : 10.1145/1401890.1401965
A survey of multi-view machine learning, Neural Computing and Applications, pp.2031-2038, 2013. ,
DOI : 10.1007/s00521-013-1362-6
Learning User's Confidence for Active Learning, IEEE Transactions on Geoscience and Remote Sensing, vol.51, issue.2, pp.872-880, 2013. ,
DOI : 10.1109/TGRS.2012.2203605
Active learning from crowds, International Conference on Machine Learning, pp.1161-1168, 2011. ,
Cleansing Noisy Data Streams, 2008 Eighth IEEE International Conference on Data Mining, pp.1139-1144, 2008. ,
DOI : 10.1109/ICDM.2008.45
URL : https://opus.lib.uts.edu.au/bitstream/10453/10791/1/2008001693OK.pdf
Active Learning With Drifting Streaming Data, IEEE transactions on neural networks and learning systems, pp.27-39, 2014. ,
DOI : 10.1109/TNNLS.2012.2236570