Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Measuring the VC-dimension of a learning machine
Neural Computation
Approximation and Estimation Bounds for Artificial Neural Networks
Machine Learning - Special issue on computational learning theory
Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
The nature of statistical learning theory
The nature of statistical learning theory
Discovering informative patterns and data cleaning
Advances in knowledge discovery and data mining
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Optimal control by least squares support vector machines
Neural Networks
Support Vector Mixture for Classification and Regression Problems
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
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Hardenability prediction is very difficult in the steel refining process. Based on the idea that the accuracy of model can be significantly improved by combining several sub-models, a multiple support vector machine(MSVM) based hardenability prediction model is proposed in this paper. The influence factors of hardenability are analysised to determines the number of sub-model and the input variables of the sub-model. In order to improve the precision and generalization capability of the prediction model, genetic algorithm (GA) is adopted to optimize the parameters of MSVM. The simulation results demonstrate the efficiency of the method.