Automated design of linear tree classifiers
Pattern Recognition
The nature of statistical learning theory
The nature of statistical learning theory
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Incremental Support Vector Machine Construction
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
Construction and Application of PSO-SVM Model for Personal Credit Scoring
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
A PSO-based framework for dynamic SVM model selection
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Automatic model selection for the optimization of SVM kernels
Pattern Recognition
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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
An experimental bias-variance analysis of SVM ensembles based on resampling techniques
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Incremental training of support vector machines
IEEE Transactions on Neural Networks
Gaussian mixture modeling for dynamic particle swarm optimization of recurrent problems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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The Support Vector Machine (SVM) is a very powerful technique for general pattern recognition purposes but its efficiency in practice relies on the optimal selection of hyper-parameters. A naive or ad hoc choice of values for these can lead to poor performance in terms of generalization error and high complexity of the parameterized models obtained in terms of the number of support vectors identified. The task of searching for optimal hyper-parameters with respect to the aforementioned performance measures is the so-called SVM model selection problem. In this paper we propose a strategy to select optimal SVM models in a dynamic fashion in order to address this problem 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 favor of revised models. This strategy combines the power of 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.