Uncertainty in Artificial Intelligence
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CoRE Kernels
Ping Li
Abstract:
The term ‚??CoRE kernel‚?Ě stands for correlation- resemblance kernel. In many real-world applica- tions (e.g., computer vision), the data are often high-dimensional, sparse, and non-binary. We propose two types of (nonlinear) CoRE kernels for non-binary sparse data and demonstrate the effectiveness of the new kernels through a clas- sification experiment. CoRE kernels are sim- ple with no tuning parameters. However, train- ing nonlinear kernel SVM can be costly in time and memory and may not be always suitable for truly large-scale industrial applications (e.g., search). In order to make the proposed CoRE kernels more practical, we develop basic proba- bilistic hashing (approximate) algorithms which transform nonlinear kernels into linear kernels.
Keywords:
Pages: 496-504
PS Link:
PDF Link: /papers/14/p496-li.pdf
BibTex:
@INPROCEEDINGS{Li14,
AUTHOR = "Ping Li ",
TITLE = "CoRE Kernels",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "496--504"
}


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