Comparison of four support-vector based function approximators


Kruif, Bas J. de and Vries, Theo J.A. de (2004) Comparison of four support-vector based function approximators. In: IEEE International Joint Conference on Neural Networks, 2004, 25-29 July 2004, Budapest, Hungary (pp. pp. 549-554).

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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
Electrical Engineering, Mathematics and Computer Science (EEMCS)
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