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Abstract: We present methods for dealing with missing variables in the context of Gaussian Processes and Support Vector Machines. This solves an important problem which has largely been ignored by kernel methods: How to systematically deal with incomplete data? Our method can also be applied to problems with partially observed labels as well as to the transductive setting where we view the labels as missing data. (Update)
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BibTeX entry: (Update)
A. J. Smola, S. V. N. Vishwanathan, and T. Hofmann. Kernel methods for missing variables. In Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005. http://citeseer.ist.psu.edu/smola05kernel.html More
@misc{ smola05kernel,
author = "A. Smola and S. Vishwanathan and T. Hofmann",
title = "Kernel methods for missing variables",
text = "A. J. Smola, S. V. N. Vishwanathan, and T. Hofmann. Kernel methods for
missing variables. In Proceedings of the Tenth International Workshop on
Artificial Intelligence and Statistics, 2005.",
year = "2005",
url = "citeseer.ist.psu.edu/smola05kernel.html" }
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96 Backpropagation applied to handwritten zip code recognition (context) - LeCun, Boser et al. - 1989
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76 Supervised learning from incomplete data via an EM approach - Ghahramani, Jordan - 1994
55 Oxford University Press (context) - Lauritzen - 1996
47 Sparse greedy matrix approximation for machine learning - Smola, Scholkopf - 2000
45 Support Vector Learning (context) - Scholkopf - 1997
16 The Art of Scientific Computation (context) - Press, Teukolsky et al. - 1994
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