CoRE Kernels

Ping Li Rutgers University
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:863-871, 2014.

Abstract

The term “CoRE kernel” stands for correlationresemblance kernel. In many real-world applications (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 classification experiment. CoRE kernels are simple with no tuning parameters. However, training 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 probabilistic hashing (approximate) algorithms which transform nonlinear kernels into linear kernels.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-university14x, title = {CoRE Kernels}, author = {University, Ping Li Rutgers}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {863--871}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/university14x/university14x.pdf}, url = {https://proceedings.mlr.press/r12/university14x.html}, abstract = {The term “CoRE kernel” stands for correlationresemblance kernel. In many real-world applications (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 classification experiment. CoRE kernels are simple with no tuning parameters. However, training 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 probabilistic hashing (approximate) algorithms which transform nonlinear kernels into linear kernels.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T CoRE Kernels %A Ping Li Rutgers University %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-university14x %I PMLR %P 863--871 %U https://proceedings.mlr.press/r12/university14x.html %V R12 %X The term “CoRE kernel” stands for correlationresemblance kernel. In many real-world applications (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 classification experiment. CoRE kernels are simple with no tuning parameters. However, training 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 probabilistic hashing (approximate) algorithms which transform nonlinear kernels into linear kernels. %Z Reissued by PMLR on 04 October 2026.
APA
University, P.L.R.. (2014). CoRE Kernels. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:863-871 Available from https://proceedings.mlr.press/r12/university14x.html. Reissued by PMLR on 04 October 2026.

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