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|Title:||Support vector machine classification of physical and biological datasets||Authors:||Cai, C.-Z.
Support vector machine
|Issue Date:||Jun-2003||Citation:||Cai, C.-Z., Wang, W.-L., Chen, Y.-Z. (2003-06). Support vector machine classification of physical and biological datasets. International Journal of Modern Physics C 14 (5) : 575-585. ScholarBank@NUS Repository. https://doi.org/10.1142/S0129183103004759||Abstract:||The support vector machine (SVM) is used in the classification of sonar signals and DNA-binding proteins. Our study on the classification of sonar signals shows that SVM produces a result better than that obtained from other classification methods, which is consistent from the findings of other studies. The testing accuracy of classification is 95.19% as compared with that of 90.4% from multilayered neural network and that of 82.7% from nearest neighbor classifier. From our results on the classification of DNA-binding proteins, one finds that SVM gives a testing accuracy of 82.32%, which is slightly better than that obtained from an earlier study of SVM classification of protein-protein interactions. Hence, our study indicates the usefulness of SVM in the identification of DNA-binding proteins. Further improvements in SVM algorithm and parameters are suggested.||Source Title:||International Journal of Modern Physics C||URI:||http://scholarbank.nus.edu.sg/handle/10635/53199||ISSN:||01291831||DOI:||10.1142/S0129183103004759|
|Appears in Collections:||Staff Publications|
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