Active learning support vector machines with low-rank transformation
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G. Baudat and F. Anouar, Kernel-based methods and function approximation, In Neural Networks, 2001. Proceedings. IJCNN’01. International Joint Conference on, volume 2, IEEE, 2001, pages 1244–1249.
Bodó, 2011, Active learning with clustering, Active Learning and Experimental Design@ AISTATS, 127
Bordes, 2005, Fast kernel classifiers with online and active learning, Journal of Machine Learning Research, 6, 1579
Burges, 1998, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2, 121, 10.1023/A:1009715923555
Candès, 2011, Robust principal component analysis, Journal of the ACM (JACM), 58, 11, 10.1145/1970392.1970395
Chang, 2011, Libsvm: a library for support vector machines, ACM Transactions on Intelligent Systems and Technology (TIST), 2, 27
Curtin, 2013, Mlpack: A scalable c++ machine learning library, Journal of Machine Learning Research, 14, 801
S. Dasgupta and D. Hsu, Hierarchical sampling for active learning, In Proceedings of the 25th International Conference on Machine Learning, ACM, 2008, pages 208–215.
Elhamifar, 2013, Sparse subspace clustering: Algorithm, theory and applications, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 2765, 10.1109/TPAMI.2013.57
Fu, 2015, A batch-mode active learning svm method based on semi-supervised clustering, Intelligent Data Analysis, 19, 345, 10.3233/IDA-150720
W. Guo, C. Zhong and Y. Yang, Spectral clustering based active learning with applications to text classification, In MATEC Web of Conferences, volume 56. EDP Sciences, 2016.
Hoi, 2009, Semisupervised svm batch mode active learning with applications to image retrieval, ACM Transactions on Information Systems (TOIS), 27, 16, 10.1145/1508850.1508854
Hsu, 2002, A comparison of methods for multiclass support vector machines, IEEE transactions on Neural Networks, 13, 415, 10.1109/72.991427
Hu, 2009, Unsupervised active learning based on hierarchical graph-theoretic clustering, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 39, 1147, 10.1109/TSMCB.2009.2013197
Janssen, 2013, Monte-carlo based uncertainty analysis: Sampling efficiency and sampling convergence, Reliability Engineering & System Safety, 109, 123, 10.1016/j.ress.2012.08.003
A.J. Joshi, F. Porikli and N. Papanikolopoulos, Multi-class active learning for image classification, In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, IEEE, 2009, pages 2372–2379.
Lee, 2013, A study on l2-loss (squared hinge-loss) multiclass svm, Neural Computation, 25, 1302, 10.1162/NECO_a_00434
Nissim, 2014, Novel active learning methods for enhanced pc malware detection in windows os, Expert Systems with Applications, 41, 5843, 10.1016/j.eswa.2014.02.053
J. Platt et al., Sequential minimal optimization: A fast algorithm for training support vector machines, 1998.
R.B. Prudencio, C. Soares and T.B. Ludermir, Uncertainty sampling methods for selecting datasets in active meta-learning, In Neural Networks (IJCNN), The 2011 International Joint Conference on, IEEE, 2011, pages 1082–1089.
Qiu, 2015, Learning transformations for clustering and classification, Journal of Machine Learning Research, 16, 187
Recht, 2010, Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization, SIAM Review, 52, 471, 10.1137/070697835
Roy, 2001, Toward optimal active learning through monte carlo estimation of error reduction, ICML, Williamstown, 441
C. Sanderson, Armadillo: An open source c++ linear algebra library for fast prototyping and computationally intensive experiments, 2010.
Settles, 2008, Multiple-instance active learning, Advances in Neural Information Processing Systems, 1289
H. Shao, B. Tong and E. Suzuki, Query by committee in a heterogeneous environment, In International Conference on Advanced Data Mining and Applications, Springer, 2012, pp. 186–198.
Sivaraman, 2010, A general active-learning framework for on-road vehicle recognition and tracking, IEEE Transactions on Intelligent Transportation Systems, 11, 267, 10.1109/TITS.2010.2040177
Sriperumbudur, 2012, A proof of convergence of the concave-convex procedure using zangwill’s theory, Neural Computation, 24, 1391, 10.1162/NECO_a_00283
Q. Wang, W. Guo, K. Zhang, I. Ororbia, G. Alexander, X. Xing, C.L. Giles and X. Liu, Learning adversary-resistant deep neural networks, arXiv preprint arXiv:1612.01401, 2016.
Q. Wang, W. Guo, K. Zhang, X. Xing, C.L. Giles and X. Liu, Random feature nullification for adversary resistant deep architecture, arXiv preprint arXiv:1610.01239, 2016.
Watson, 1992, Characterization of the subdifferential of some matrix norms, Linear Algebra and Its Applications, 170, 33, 10.1016/0024-3795(92)90407-2
Wolfe, 1961, A duality theorem for non-linear programming, Quarterly of applied mathematics, 239, 10.1090/qam/135625
J. Yang et al., Automatically labeling video data using multi-class active learning, In Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on, IEEE, 2003, pp. 516–523.
