Name
Abstract | ||
|---|---|---|
The selection for hyper-parameters including kernel parameters and the regularization is important to the performance of least squares support vector machines (LS-SVM). The existed parameters selection algorithms, such as the analytical, algebraic techniques and particle swarm optimization (PSO) algorithm, have their own shortcomings. In this paper, the problem of model selection for LS-SVM is discussed. A new method selecting the LS-SVM hyper-parameters is proposed based on the principles of the quantum-behaved particle swarm optimization (QPSO). The feasibility of this method is evaluated on data sets produced by sinc function. Experimental results show that LS-SVM of QPSO-based hyper-parameters selection obtains better generalization capability and has more fast convergence speed than PSO-based hyper-parameters selection. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICNC.2008.410 | ICNC |
Keywords | Field | DocType |
quantum-behaved particle swarm optimization,parameter selection,ls-svm regression,generalization capability,model selection,regression analysis,least squares support vector machines,particle swarm optimisation,algebraic techniques,least squares approximations,vector machines,squares support,qpso-based hyper-parameters selection obtains,algebra,qpso-based hyperparameters selection,qpso-based hyper-parameters selection,parameters selection algorithm,pso-based hyper-parameters selection,support vector machines,new method,particle swarm optimization,ls-svm hyper-parameters,convergence,least squares support vector machine,kernel,artificial neural networks | Kernel (linear algebra),Least squares,Particle swarm optimization,Truncation selection,Mathematical optimization,Sinc function,Computer science,Support vector machine,Model selection,Artificial intelligence,Artificial neural network,Machine learning | Conference |
Volume | ISBN | Citations |
2 | 978-0-7695-3304-9 | 1 |
PageRank | References | Authors |
0.34 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lincheng Zhou | 1 | 27 | 3.92 |
| Hui-zhong Yang | 2 | 1 | 1.35 |
| Chun-bo Liu | 3 | 1 | 0.34 |