Semi-supervised Learning via gaussian processes

Advances in Neural Information Processing Systems

Volumn , Issue , 2005, Pages

  Lawrence, Neil D ^a   Jordan, Michael I ^b  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); PATTERN RECOGNITION;

GAUSSIAN PROCESSES; HANDWRITTEN DIGIT; NOISE MODELING; NOISE MODELS; PROBABILISTIC APPROACHES; SEMI-SUPERVISED CLASSIFICATION; SEMI-SUPERVISED LEARNING; UNLABELED DATA;

SUPERVISED LEARNING;

EID: 78049527893     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. A. Agresti. Categorical Data Analysis. John Wiley and Sons, 2002.
2. O. Chapelle, J. Weston, and B. Scholkopf. Cluster kernels for semi-supervised learning. In Advances in Neural Information Processing Systems, Cambridge, MA, 2002. MIT Press.
3. L. Csato. Gaussian Processes Iterative Sparse Approximations. PhD thesis, Aston 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. Scholkopf. Estimating a kernel Fisher discriminant in the 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.