Continuous Optimization based-on Boosting Gaussian Mixture Model

Bin Lin, Xian-ji Wang, Run-tian Zhong, Zhen-quan Zhuang
2006 18th International Conference on Pattern Recognition (ICPR'06)  
A new Estimation of Distribution Algorithm(EDA) based-on Gaussian Mixture Model (GMM) is proposed, in which boosting, an efficient ensemble learning method, is adopted to estimate GMM. By boosting simple GMM with two components, it has the ability of learning the model structure and parameters automatically without any requirement for prior knowledge. Moreover, since boosting can be viewed as a gradient search for a good fit of some objective in function space, the new EDA is time efficient. A
more » ... et of experiments is implemented to evaluate the efficiency and performance of the new algorithm. The results show that, with a relatively smaller population and less number of generations, the new algorithm can perform as well as compared EDAs in optimizing multimodal functions.
doi:10.1109/icpr.2006.412 dblp:conf/icpr/LinWZZ06 fatcat:2hyrvkloaffl5g3gc6nwh6w5rq