A New Frontier of Kernel Design for Structured Data

Kilho Shin
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(1):401-409, 2013.

Abstract

Many kernels for discretely structured data in the literature are designed within the framework of the convolution kernel and its generalization, the mapping kernel. The two most important advantages to use this framework is an easy-to-check criteria of positive definiteness and efficient computation based on the dynamic programming methodology of the resulting kernels. On the other hand, the recent theory of partitionable kernels reveals that the known kernels only take advantage of a very small portion of the potential of the framework. In fact, we have good opportunities to find novel and important kernels in the unexplored area. In this paper, we shed light on a novel important class of kernels within the framework: We give a mathematical characterization of the class, show a parametric method to optimize kernels of the class to specific problems, based on this characterization, and present some experimental results, which show the new kernels are promising in both accuracy and efficiency.

Cite this Paper

BibTeX
@InProceedings{pmlr-v28-shin13, title = {A New Frontier of Kernel Design for Structured Data}, author = {Shin, Kilho}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {401--409}, year = {2013}, editor = {Dasgupta, Sanjoy and McAllester, David}, volume = {28}, number = {1}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/shin13.pdf}, url = {https://proceedings.mlr.press/v28/shin13.html}, abstract = {Many kernels for discretely structured data in the literature are designed within the framework of the convolution kernel and its generalization, the mapping kernel. The two most important advantages to use this framework is an easy-to-check criteria of positive definiteness and efficient computation based on the dynamic programming methodology of the resulting kernels. On the other hand, the recent theory of partitionable kernels reveals that the known kernels only take advantage of a very small portion of the potential of the framework. In fact, we have good opportunities to find novel and important kernels in the unexplored area. In this paper, we shed light on a novel important class of kernels within the framework: We give a mathematical characterization of the class, show a parametric method to optimize kernels of the class to specific problems, based on this characterization, and present some experimental results, which show the new kernels are promising in both accuracy and efficiency. } }
Endnote
%0 Conference Paper %T A New Frontier of Kernel Design for Structured Data %A Kilho Shin %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-shin13 %I PMLR %P 401--409 %U https://proceedings.mlr.press/v28/shin13.html %V 28 %N 1 %X Many kernels for discretely structured data in the literature are designed within the framework of the convolution kernel and its generalization, the mapping kernel. The two most important advantages to use this framework is an easy-to-check criteria of positive definiteness and efficient computation based on the dynamic programming methodology of the resulting kernels. On the other hand, the recent theory of partitionable kernels reveals that the known kernels only take advantage of a very small portion of the potential of the framework. In fact, we have good opportunities to find novel and important kernels in the unexplored area. In this paper, we shed light on a novel important class of kernels within the framework: We give a mathematical characterization of the class, show a parametric method to optimize kernels of the class to specific problems, based on this characterization, and present some experimental results, which show the new kernels are promising in both accuracy and efficiency.
RIS
TY - CPAPER TI - A New Frontier of Kernel Design for Structured Data AU - Kilho Shin BT - Proceedings of the 30th International Conference on Machine Learning DA - 2013/02/13 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-shin13 PB - PMLR DP - Proceedings of Machine Learning Research VL - 28 IS - 1 SP - 401 EP - 409 L1 - http://proceedings.mlr.press/v28/shin13.pdf UR - https://proceedings.mlr.press/v28/shin13.html AB - Many kernels for discretely structured data in the literature are designed within the framework of the convolution kernel and its generalization, the mapping kernel. The two most important advantages to use this framework is an easy-to-check criteria of positive definiteness and efficient computation based on the dynamic programming methodology of the resulting kernels. On the other hand, the recent theory of partitionable kernels reveals that the known kernels only take advantage of a very small portion of the potential of the framework. In fact, we have good opportunities to find novel and important kernels in the unexplored area. In this paper, we shed light on a novel important class of kernels within the framework: We give a mathematical characterization of the class, show a parametric method to optimize kernels of the class to specific problems, based on this characterization, and present some experimental results, which show the new kernels are promising in both accuracy and efficiency. ER -
APA
Shin, K.. (2013). A New Frontier of Kernel Design for Structured Data. Proceedings of the 30th International Conference on Machine Learning, in Proceedings of Machine Learning Research 28(1):401-409 Available from https://proceedings.mlr.press/v28/shin13.html.

Related Material