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 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.

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 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.}, 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 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. %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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