关键词: 土地集约利用, 相关系数, 蚁群算法, 支持向量机, 评价

Abstract: Based on relevant literature research of evaluation on intensive land-use both at home and abroad, the theory of Support Vector Machine (SVM) and Ant Colony Algorithm (ACO) was discussed. A new method of Correlation Coefficient, the Ant Colony Algorithm and Support Vector Machine (cACO-SVM) was proposed, which analyzed the relevant indicators to determine index set, using ACO, optimization of SVM parameters to draw a good penalty factor C and kernel function sigma and epsilon insensitive coefficient and training SVM, the method improved the training accuracy. Optimization of the land intensive utilization evaluation based on cACO-SVM was put forward, comparing with the ACO-SVM and GA - SVM intensive land use evaluation. Evaluation and simulation results show that analysis of cACO-SVM intensive land use evaluation is better than that of the ACO - SVM and GA - two methods of SVM. Intensive land use evaluation effect of cACO - SVM is more ideal.

Key words: intensive land use, correlation coefficient, ACO, SVM, evaluation