nips nips2004 nips2004-166 nips2004-166-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Neil D. Lawrence, Michael I. Jordan
Abstract: We present a probabilistic approach to learning a Gaussian Process classifier in the presence of unlabeled data. Our approach involves a “null category noise model” (NCNM) inspired by ordered categorical noise models. The noise model reflects an assumption that the data density is lower between the class-conditional densities. We illustrate our approach on a toy problem and present comparative results for the semi-supervised classification of handwritten digits. 1
[1] A. Agresti. Categorical Data Analysis. John Wiley and Sons, 2002.
[2] O. Chapelle, J. Weston, and B. Sch¨lkopf. Cluster kernels for semi-supervised learno ing. In Advances in Neural Information Processing Systems, Cambridge, MA, 2002. MIT Press.
[3] L. Csat´. Gaussian Processes — Iterative Sparse Approximations. PhD thesis, Aston o University, 2002.
[4] T. Joachims. Making large-scale SVM learning practical. In Advances in Kernel Methods: Support Vector Learning, Cambridge, MA, 1998. MIT Press.
[5] N. D. Lawrence and B. Sch¨lkopf. Estimating a kernel Fisher discriminant in the o presence of label noise. In Proceedings of the International Conference in Machine Learning, San Francisco, CA, 2001. Morgan Kaufmann.
[6] N. D. Lawrence, M. Seeger, and R. Herbrich. Fast sparse Gaussian process methods: The informative vector machine. In Advances in Neural Information Processing Systems, Cambridge, MA, 2003. MIT Press.
[7] T. P. Minka. A family of algorithms for approximate Bayesian inference. PhD thesis, Massachusetts Institute of Technology, 2001.
[8] M. Seeger. Covariance kernels from Bayesian generative models. In Advances in Neural Information Processing Systems, Cambridge, MA, 2002. MIT Press.
[9] P. Sollich. Probabilistic interpretation and Bayesian methods for support vector machines. In Proceedings 1999 International Conference on Artificial Neural Networks, ICANN’99, pages 91–96, 1999.
[10] V. N. Vapnik. Statistical Learning Theory. John Wiley and Sons, New York, 1998.
[11] C. K. I. Williams. Prediction with Gaussian processes: From linear regression to linear prediction and beyond. In Learning in Graphical Models, Cambridge, MA, 1999. MIT Press.