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CoRE Kernels
Ping Li
Abstract:
The term ??CoRE kernel? 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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