nips nips2006 nips2006-169 nips2006-169-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Wei Chu, Vikas Sindhwani, Zoubin Ghahramani, S. S. Keerthi
Abstract: Correlation between instances is often modelled via a kernel function using input attributes of the instances. Relational knowledge can further reveal additional pairwise correlations between variables of interest. In this paper, we develop a class of models which incorporates both reciprocal relational information and input attributes using Gaussian process techniques. This approach provides a novel non-parametric Bayesian framework with a data-dependent covariance function for supervised learning tasks. We also apply this framework to semi-supervised learning. Experimental results on several real world data sets verify the usefulness of this algorithm. 1
Bar-Hillel, A., Hertz, T., Shental, N., & Weinshall, D. (2003). Learning distance functions using equivalence relations. Proceedings of International Conference on Machine Learning (pp. 11–18). Basu, S., Bilenko, M., & Mooney, R. J. (2004). A probabilisitic framework for semi-supervised clustering. Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 59–68). Chapelle, O., Weston, J., & Sch¨ lkopf, B. (2003). Cluster kernels for semi-supervised learning. Neural Inforo mation Processing Systems 15 (pp. 585–592). Getoor, L., Friedman, N., Koller, D., & Taskar, B. (2002). Learning probabilistic models of link structure. Journal of Machine Learning Research, 3, 679–707. Kapoor, A., Qi, Y., Ahn, H., & Picard, R. (2005). Hyperparameter and kernel learning for graph-based semisupervised classification. Neural Information Processing Systems 18. Krishnapuram, B., Williams, D., Xue, Y., Carin, L., Hartemink, A., & Figueiredo, M. (2004). On semisupervised classification. Neural Information Processing Systems (NIPS). Lawrence, N. D., & Jordan, M. I. (2005). Semi-supervised learning via Gaussian processes. Advances in Neural Information Processing Systems 17 (pp. 753–760). Minka, T. P. (2001). A family of algorithms for approximate Bayesian inference. Ph.D. thesis, Massachusetts Institute of Technology. Opper, M., & Winther, O. (2005). Expectation consistent approximate inference. Journal of Machine Learning Research, 2117–2204. Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. The MIT Press. Seeger, M. (2003). Bayesian Gaussian process models: PAC-Bayesian generalisation error bounds and sparse approximations. Doctoral dissertation, University of Edinburgh. Sindhwani, V., Chu, W., & Keerthi, S. S. (2007). Semi-supervised Gaussian process classification. The Twentieth International Joint Conferences on Artificial Intelligence. to appear. Sindhwani, V., Niyogi, P., & Belkin, M. (2005). Beyound the point cloud: from transductive to semi-supervised learning. Proceedings of the 22th International Conference on Machine Learning (pp. 825–832). Taskar, B., Abbeel, P., & Koller, D. (2002). Discriminative probabilistic models for relational data. Proceedings of Conference on Uncertainty in Artificial Intelligence. Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (2001). Constrained k-means clustering with background knowledge. Proceedings of International Conference on Machine Learning (pp. 577–584). Wainwright, M. J., Jaakkola, T., & Willsky, A. S. (2005). A new class of upper bounds on the log partition function. IEEE Trans. on Information Theory, 51, 2313–2335. Yu, K., Chu, W., Yu, S., Tresp, V., & Xu, Z. (2006). Stochastic relational models for discriminative link prediction. Advances in Neural Information Processing Systems. to appear. Zhou, D., Bousquet, O., Lal, T., Weston, J., & Sch¨ lkopf, B. (2004). Learning with local and global consistency. o Advances in Neural Information Processing Systems 18 (pp. 321–328). Zhu, X., Ghahramani, Z., & Lafferty, J. (2003). Semi-supervised learning using Gaussian fields and harmonic functions. Proceedings of the 20th International Conference on Machine Learning.