nips nips2007 nips2007-175 nips2007-175-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Qiuhua Liu, Xuejun Liao, Lawrence Carin
Abstract: A semi-supervised multitask learning (MTL) framework is presented, in which M parameterized semi-supervised classifiers, each associated with one of M partially labeled data manifolds, are learned jointly under the constraint of a softsharing prior imposed over the parameters of the classifiers. The unlabeled data are utilized by basing classifier learning on neighborhoods, induced by a Markov random walk over a graph representation of each manifold. Experimental results on real data sets demonstrate that semi-supervised MTL yields significant improvements in generalization performance over either semi-supervised single-task learning (STL) or supervised MTL. 1
[1] B. Bakker and T. Heskes. Task clustering and gating for Bayesian multitask learning. Journal of Machine Learning Research, pages 83–99, 2003.
[2] D. Blackwell and J. MacQueen. Ferguson distributions via polya urn schemes. Annals of Statistics, 1: 353–355, 1973.
[3] R. Caruana. Multitask learning. Machine Learning, 28:41–75, 1997.
[4] F. R. K. Chung. Spectral Graph Theory. American Mathematical Society, 1997.
[5] T. Evgeniou and M. Pontil. Regularized multi-task learning. In Proc. 17th SIGKDD Conf. on Knowledge Discovery and Data Mining, 2004.
[6] T. Ferguson. A Bayesian analysis of some nonparametric problems. Annals of Statistics, 1:209–230, 1973.
[7] J. Hanley and B. McNeil. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143:29–36, 1982.
[8] G. E. Hinton and T. J. Sejnowski. Learning and relearning in boltzmann machines. In J. L. McClelland, D. E. Rumelhart, and the PDP Research Group, editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, volume 1, pages 282–317. MIT Press, Cambridge, MA, 1986.
[9] T. Joachims. Transductive inference for text classification using support vector machines. In Proc. 16th International Conf. on Machine Learning (ICML), pages 200–209. Morgan Kaufmann, San Francisco, CA, 1999.
[10] B. Krishnapuram, D. Williams, Y. Xue, A. Hartemink, L. Carin, and M. Figueiredo. On semi-supervised classification. In Advances in Neural Information Processing Systems (NIPS), 2005.
[11] D.J. Newman, S. Hettich, C.L. Blake, and C.J. Merz. UCI repository of machine learning databases. http://www.ics.uci.edu/∼mlearn/MLRepository.html, 1998.
[12] M. Szummer and T. Jaakkola. Partially labeled classification with markov random walks. In Advances in Neural Information Processing Systems (NIPS), 2002.
[13] Y. Xue, X. Liao, L. Carin, and B. Krishnapuram. Multi-task learning for classification with dirichlet process priors. Journal of Machine Learning Research (JMLR), 8:35–63, 2007.
[14] K. Yu, A. Schwaighofer, V. Tresp, W.-Y. Ma, and H.J. Zhang. Collaborative ensemble learning: Combining collaborative and content-based information filtering via hierarchical bayes. In Proceedings of the 19th International Conference on Uncertainty in Artificial Intelligence (UAI 2003), 2003.
[15] X. Zhu, Z. Ghahramani, and J. Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. In The Twentieth International Conference on Machine Learning (ICML), 2003.