Automated design of linear tree classifiers
Pattern Recognition
The nature of statistical learning theory
The nature of statistical learning theory
Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass SVM Model Selection Using Particle Swarm Optimization
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Adaptive particle swarm optimization: detection and response to dynamic systems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Multi-Objective Optimization for SVM Model Selection
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Real-time data mining of non-stationary data streams from sensor networks
Information Fusion
Automatic model selection for the optimization of SVM kernels
Pattern Recognition
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
A high throughput system for intelligent watermarking of bi-tonal images
Applied Soft Computing
A dynamic model selection strategy for support vector machine classifiers
Applied Soft Computing
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Support Vector Machines (SVM) are very powerful classifiers in theory but their efficiency in practice rely on an optimal selection of hyper-parameters. A naïve or ad hoc choice of values for the latter can lead to poor performance in terms of generalization error and high complexity of parameterized models obtained in terms of the number of support vectors identified. This hyper-parameter estimation with respect to the aforementioned performance measures is often called the model selection problem in the SVM research community. In this paper we propose a strategy to select optimal SVM models in a dynamic fashion in order to attend that when knowledge about the environment is updated with new observations and previously parameterized models need to be re-evaluated, and in some cases discarded in favour of revised models. This strategy combines the power of the swarm intelligence theory with the conventional grid-search method in order to progressively identify and sort out potential solutions using dynamically updated training datasets. Experimental results demonstrate that the proposed method outperforms the traditional approaches tested against it while saving considerable computational time.