Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Time Series Prediction Using Nonlinear Support Vector Regression Based on Classification
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Linear dependency between ε and the input noise in ε-support vector regression
IEEE Transactions on Neural Networks
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The Ɛ -Support Vector regression Machine is a promising artificial intelligence technique, in which the regression algorithm has already been used in solving the nonlinear function approach successfully. Most users select parameters for an SVM by rule of thumb, so they frequently fail to generate the optimal parameters effect for the function. This has restricted effective use of SVM to a great degree. In this paper, the authors use genetic algorithm to solve the SVM parameters optimization problem. Simulation result shows that the method has high precision and possesses certain practical application significance.