Detection of Tornados Using an Incremental Revised Support Vector Machine with Filters

Hyung-Jin Son1 and Theodore B. Trafalis1

1 School of Industrial Engineering, The University of Oklahoma, 202 W. Boyd, CEC 124, Norman, OK 73019, U.S.A.
son@ou.edu
ttrafalis@ou.edu

Abstract. Recently Support Vector Machines (SVMs) have played a leading role in pattern classification. SVMs are quite effective to classify static data in numerous applications. However, the use of SVMs in dynamically data driven application systems (DDDAS) is somewhat limited. This motivates the development of incremental approaches to handle DDDAS. In an incremental learning approach, it is critical to keep a certain number of support vectors (SVs) without seriously sacrificing the generalization performance of SVMs. In this paper a novel incremental SVM method, called an incremental revised support vector machine with filters (IRSVMF) is proposed to resolve the above limitations. Computational experiments with tornado data show that this approach is quite effective to reduce the number of SVs and computing time and to increase the detection rate of tornados.

LNCS 3993, pp. 506-513.

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