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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ferose Babu, T.A. Pradeepa, R. |
| Copyright Year | 2014 |
| Description | Author affiliation: DRDO, NPOL, Kochi, India (Ferose Babu, T.A.; Pradeepa, R.) |
| Abstract | Underwater target classification is a complex task, due to the difficulty in identifying non-overlapping and stable feature set. It is required to choose the right algorithm, approach and technique, or the best combinations of approaches and techniques from a large set of options available in the literature for the specific problem. A binary classifier can tackle the problem by decomposing multiclass problem into binary class. This paper addresses the multiclass underwater classification problem using binary classifier -- Support Vector Machine (SVM). Three methods "all-against-all," "all-against-all Hierarchical," "one-against-all"("AVA", "AVA-H", "OVA") are tried out and performance using a particular feature derived from real data set is compared. A number of metrics are used to compare the performance. OVA gives a better performance with less computation compared to other methods. |
| Starting Page | 46 |
| Ending Page | 49 |
| File Size | 146308 |
| Page Count | 4 |
| File Format | |
| e-ISBN | 9781479943630 |
| DOI | 10.1109/ICACC.2014.17 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-27 |
| Publisher Place | India |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Training Multi class classification Accuracy Support Vector Machines (SVM) Gaussian distribution Under water target classification Data mining Kernel Underwater vehicles Binary classification Decomposing multi class to Binary |
| Content Type | Text |
| Resource Type | Article |
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