A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Core Vector Regression for very large regression problems
ICML '05 Proceedings of the 22nd international conference on Machine learning
Half-Against-Half multi-class support vector machines
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Generalized Core Vector Machines
IEEE Transactions on Neural Networks
Hybrid model based on SVM with Gaussian loss function and adaptive Gaussian PSO
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
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This paper presents a core vector machine (CVM)-based algorithm for on-line voltage security assessment of power systems. To classify the system security status, a CVM has been trained for each contingency. The proposed CVM-based security assessment has very small training time and space in comparison with support vector machines (SVM) and artificial neural networks (ANNs)-based algorithms. The proposed algorithm produces less support vectors (SV) and therefore is faster than existing algorithms. In this paper, a new decision tree (DT)-based feature selection technique has been presented, too. The proposed CVM algorithm has been applied to New England 39-bus power system. The simulation results show the effectiveness and the stability of the proposed method for on-line voltage security assessment procedure of large-scale power system.