Abstract
Study on Generalization Capability of Support Vector Machine in Splice Site Type Recognition of DNA Sequence. Recently, support vector machine has become a popular model as machine learning. A particular advantage of SVM over other machine learning is that it can be analyzed theoretically and at same time can achieve a good performance when applied to real problems. This paper will describe analytically the using of SVM to solve pattern recognition problem with a preliminary case study in determining the type of splice site on the DNA sequence, particularity on the generalization capability. The result obtained show that SVM has a good generalization capability of around 95.4 %.
References
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Recommended Citation
Kerami, Djati and Murfi, Hendri
(2004)
"KAJIAN KEMAMPUAN GENERALISASI SUPPORT VECTOR MACHINE DALAM PENGENALAN JENIS SPLICE SITES PADA BARISAN DNA,"
Makara Journal of Science: Vol. 8:
Iss.
3, Article 2.
Available at:
https://scholarhub.ui.ac.id/science/vol8/iss3/2
