Please use this identifier to cite or link to this item:
https://doi.org/10.1142/S0129183103004759
DC Field | Value | |
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dc.title | Support vector machine classification of physical and biological datasets | |
dc.contributor.author | Cai, C.-Z. | |
dc.contributor.author | Wang, W.-L. | |
dc.contributor.author | Chen, Y.-Z. | |
dc.date.accessioned | 2014-05-19T02:55:31Z | |
dc.date.available | 2014-05-19T02:55:31Z | |
dc.date.issued | 2003-06 | |
dc.identifier.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 | |
dc.identifier.issn | 01291831 | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/53199 | |
dc.description.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. | |
dc.description.uri | http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1142/S0129183103004759 | |
dc.source | Scopus | |
dc.subject | Algorithm | |
dc.subject | Classifier | |
dc.subject | DNA-binding proteins | |
dc.subject | Neural network | |
dc.subject | Sonar signal | |
dc.subject | Support vector machine | |
dc.type | Article | |
dc.contributor.department | COMPUTATIONAL SCIENCE | |
dc.contributor.department | BIOPROCESSING TECHNOLOGY CENTRE | |
dc.description.doi | 10.1142/S0129183103004759 | |
dc.description.sourcetitle | International Journal of Modern Physics C | |
dc.description.volume | 14 | |
dc.description.issue | 5 | |
dc.description.page | 575-585 | |
dc.identifier.isiut | 000186686700005 | |
Appears in Collections: | Staff Publications |
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