Weighted Kernel Functions for SVM Learning in String Domains: A Distance Function Viewpoint (2005)
| Venue: | In Proceedings of ICMLC (International Conference on Machine Learning and Cybernetics |
| Citations: | 2 - 2 self |
BibTeX
@INPROCEEDINGS{Vanschoenwinkel05weightedkernel,
author = {Bram Vanschoenwinkel and Feng Liu and Bernard Manderick},
title = {Weighted Kernel Functions for SVM Learning in String Domains: A Distance Function Viewpoint},
booktitle = {In Proceedings of ICMLC (International Conference on Machine Learning and Cybernetics},
year = {2005}
}
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Abstract
This paper extends the idea of weighted distance functions to kernels and support vector machines. Here, we focus on applications that rely on sliding a window over a sequence of string data. For this type of problems it is argued that a symbolic, context-based representation of the data should be preferred over a continuous, real format as this is a much more intuitive setting for working with (weighted) distance functions. It is shown how a weighted string distance can be decomposed and subsequently used in di#erent kernel functions and how these kernel functions correspond to inner products between real vectors. As a case-study named entity recognition is used with information gain ratio as a weighting scheme.
