Comparison of four support-vector based function approximators

Kruif de, Bas J. and Vries de, Theo J.A. (2004) Comparison of four support-vector based function approximators. In: IEEE International Joint Conference on Neural Networks, 2004, 25-29 July 2004, Budapest, Hungary.

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Abstract:	One of the uses of the support vector machine (SVM), as introduced in V.N. Vapnik (2000), is as a function approximator. The SVM and approximators based on it, approximate a relation in data by applying interpolation between so-called support vectors, being a limited number of samples that have been selected from this data. Several support-vector based function approximators are compared in this research. The comparison focuses on the following subjects: i) how many support vectors are involved in achieving a certain approximation accuracy, ii) how well are noisy training samples handled, and iii) how is ambiguous training data dealt with. The comparison shows that the so-called key sample machine (KSM) outperforms the other schemes, specifically on aspects i and ii. The distinctive features that explain this, are the quadratic cost function and using all the training data to train the limited parameters.
Item Type:	Conference or Workshop Item
Copyright:	©2004 IEEE
Faculty:	Electrical Engineering, Mathematics and Computer Science (EEMCS)
Research Group:	Robotics and Mechatronics (CE)
Link to this item:	http://purl.utwente.nl/publications/55809
Official URL:	http://dx.doi.org/10.1109/IJCNN.2004.1379968
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