Semi-supervised Gaussian process classifiers

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2007, Pages 1059-1064

  Sindhwani, Vikas ^a   Chu, Wei ^b   Keerthi, S Sathiya ^c  

^a THE UNIVERSITY OF CHICAGO   (United States)

^b Howard Hughes Medical Institute   (United States)

^c YAHOO RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CROSS-VALIDATION TECHNIQUE; EXPECTATION PROPAGATION; GAUSSIAN PROCESSES; GEOMETRIC PROPERTIES; KERNEL FUNCTION; LABELED AND UNLABELED DATA; MODEL SELECTION; PROBABILISTIC EXTENSION;

ARTIFICIAL INTELLIGENCE; GAUSSIAN DISTRIBUTION; MACHINERY;

GAUSSIAN NOISE (ELECTRONIC);

EID: 60949105967     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

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3. A. Kapoor, Y. Qi, H. Ahn, and R. Picard. Hyperparameter and kernel learning for graph-based semi-supervised classification. In Neural Information Processing Systems 18, 2005.
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5. N. D. Lawrence and M. Jordan. Semi-supervised learning via Gaussian processes. In Neural Information Processing Systems 17, 2004.
6. C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2006.
7. V. Sindhwani, P. Niyogi, and M. Belkin. Beyond the point cloud: from transductive to semi-supervised learning. In International Conference on Machine Learning, 2005.
8. X. Zhu, Z. Ghahramani, and J. Lafferty. Semi-supervised learning: From Gaussian fields to Gaussian processes. Technical Report CMU-CS-03-175, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 2003.