Search results for key=ZaS2010a :
1 match found.
Search my entire BibTeX database
Abstract |
BibTeX entry |
Postscript |
PDF |
Powerpoint |
Refereed full papers (journals, book chapters, international conferences)
2010
-
Nayyar Abbas Zaidi and David McG. Squire,
SVMs and Data Dependent Distance Metric,
In Proceedings of the 25th International Conference on Image
and Vision Computing New Zealand,
Queenstown, New Zealand, pp. 265-271, Institute of Electrical and Electronics Engineers (IEEE), November 8-9 2010.
Support Vector Machine (SVM) is an efficient classification
tool. Based on the principle of structured risk minimization, SVM is
designed to generalize well. But it has been shown that SVM is not
immune to the curse of dimensionality. Also SVM performance is not only
critical to the choice of kernel but also kernel parameters which are
generally tuned through computationally expensive cross-validation
procedures. Typical kernels do not have any information about the
subspace to ignore irrelevant features or making relevant features
explicit. Recently, a lot of progress has been made for learning a data
dependent distance metric for improving the efficiency of k-Nearest
Neighbor (KNN) classifier. Metric learning approaches have not been
investigated in the context of SVM. In this paper, we study the impact
of learning a data dependent distance metric on classification
performance of an SVM classifier. Our novel approach in this paper is a
formulation relying on a simple Mean Square Error (MSE) gradient based
metric learning method to tune kernel's parameters. Experiments are
conducted on major UCIML, faces and digit databases. We have found
that tuning kernel parameters through metric learning approach can
improve the classification performance of an SVM classifier.
|