Ismael, Kamarulafizam and Salleh, Shaharuddin and Najeb, J. M. and Jahangir Bakhteri, R. B. (2008) Efficient parameter selection of support vector machines. In: IFMBE Proceedings. Institute of Electrical and Electronics Engineers, New York, pp. 183-186. ISBN 978-354069138-9
Full text not available from this repository.
Official URL: http://dx.doi.org/10.1007/978-3-540-69139-6-49
Support Vector Machine (SVM) has, over the years established itself as an effective method for machine learning. SVM has strengths as such that it uses a kernel function to deal with arbitrary structured data which comprises of non-linear data sets. However, to fully optimize the benefits of using the kernel function, one will have to fine-tune the parameters of SVM in order to achieve feasible results. However, parameter selection can get complicated as the number of parameters and the size of the dataset increases. In this paper, we propose a method to deal with effective parameter selection for SVM for optimal performance through experiments done on heart sound data using the features of IEFE extraction technique.
|Item Type:||Book Section|
|Additional Information:||ISBN: 978-354069138-9; 4th Kuala Lumpur International Conference on Biomedical Engineering 2008, Biomed 2008; Kuala Lumpur; 25 June 2008 through 28 June 2008|
|Uncontrolled Keywords:||parameter selection, support vector machines (SVM), v-fold cross validation|
|Subjects:||T Technology > TS Manufactures|
|Divisions:||?? FBSK ??|
|Deposited By:||Liza Porijo|
|Deposited On:||08 Jun 2011 08:11|
|Last Modified:||08 Jun 2011 08:11|
Repository Staff Only: item control page