计算机技术与发展2012，Vol.22Issue(3)：106-109,4.
基于混合模拟退火-遗传算法和HMM的Web挖掘
Web Mining Based on Hybrid Simulated Annealing Genetic Algorithm and HMM
摘要
Abstract
The training algorithm which is used to training HMM is a sub-optimal algorithm and sensitive to initial parameters. Typical hidden Markov model often leads to sub-optimal when training it with random parameters. It is ineffective when mining Web information with typical HMM. GA has the excellent ability of global searching and has the defect of slow convergence rate. SA has the excellent ability of local searching and has the defect of randomly roaming. It combines the advantages of genetic algorithm and simulated annealing algorithm .proposes hybrid simulated annealing genetic algorithm (SGA). SGA chooses the best SGA parameters by experiment and optimizes HMM combining Baum-Welch during the course of Web mining. The experimental results show that the SGA significantly improves the performance in precision and recall.关键词
模拟退火算法/遗传算法/隐马尔可夫模型/Web挖掘Key words
simulated annealing algorithm/ genetic algorithm/ hidden Markov model/ Web mining分类
信息技术与安全科学引用本文复制引用
邹腊梅,龚向坚..基于混合模拟退火-遗传算法和HMM的Web挖掘[J].计算机技术与发展,2012,22(3):106-109,4.基金项目
湖南省教育科研基金资助项目(10C1176) （10C1176）
湖南省教育科研2011基金资助项目 （）
