The nature of statistical learning theory
The nature of statistical learning theory
Handling concept drifts in incremental learning with support vector machines
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Incremental Support Vector Machine Construction
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Incremental training of support vector machines
IEEE Transactions on Neural Networks
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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.