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A training algorithm for sparse LS-SVM using compressive sampling

Conference Paper


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Abstract


  • Least Squares Support Vector Machine (LS-SVM) has become a fundamental tool in pattern recognition and machine learning. However, the main disadvantage is lack of sparseness of solutions. In this article Compressive Sampling (CS), which addresses the sparse signal representation, is employed to find the support vectors of LS-SVM. The main difference between our work and the existing techniques is that

    the proposed method can locate the sparse topology while training. In contrast, most of the traditional methods need to train the model before finding the sparse support vectors. An experimental comparison with the standard LS-SVM and existing algorithms is given for function approximation and classification problems. The results show that the proposed method achieves comparable performance with typically a

    much sparser model.

Publication Date


  • 2010

Citation


  • Yang, J., Bouzerdoum, A. & Phung, S. (2010). A training algorithm for sparse LS-SVM using compressive sampling. IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. 2054-2057). USA: IEEE.

Scopus Eid


  • 2-s2.0-78049364222

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1819&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/808

Has Global Citation Frequency


Start Page


  • 2054

End Page


  • 2057

Place Of Publication


  • USA

Abstract


  • Least Squares Support Vector Machine (LS-SVM) has become a fundamental tool in pattern recognition and machine learning. However, the main disadvantage is lack of sparseness of solutions. In this article Compressive Sampling (CS), which addresses the sparse signal representation, is employed to find the support vectors of LS-SVM. The main difference between our work and the existing techniques is that

    the proposed method can locate the sparse topology while training. In contrast, most of the traditional methods need to train the model before finding the sparse support vectors. An experimental comparison with the standard LS-SVM and existing algorithms is given for function approximation and classification problems. The results show that the proposed method achieves comparable performance with typically a

    much sparser model.

Publication Date


  • 2010

Citation


  • Yang, J., Bouzerdoum, A. & Phung, S. (2010). A training algorithm for sparse LS-SVM using compressive sampling. IEEE International Conference on Acoustics, Speech, and Signal Processing (pp. 2054-2057). USA: IEEE.

Scopus Eid


  • 2-s2.0-78049364222

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1819&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/808

Has Global Citation Frequency


Start Page


  • 2054

End Page


  • 2057

Place Of Publication


  • USA