{"title":"基于高斯模型的差分进化模体的进化学习","authors":"Peng Guo, Naixiang Li, Tonghai Liu","doi":"10.1109/ICCET.2010.5486115","DOIUrl":null,"url":null,"abstract":"In this paper, we present an approach for evolutionary learning of motif in biopolymer sequences. The focuses in this paper is evolutionary inference of Gaussian model, Differential Evolution for optimization and Markov chain Monte Carlo(MCMC) for sampling are applied in the probability learning of Gaussian model. The framework involves calculations of corresponding weight, mean and covariance. To obtain satisfied effect of MCMC sampling, the fitness function is discussed for MCMC ratio. Comparisons between results of Differential Evolution and Differential Evolution MCMC are provided to show novel effect of our method on synthetic dataset and real world dataset.","PeriodicalId":271757,"journal":{"name":"2010 2nd International Conference on Computer Engineering and Technology","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Notice of RetractionEvolutionary learning of Gaussian model for motifs with differential evolution MCMC\",\"authors\":\"Peng Guo, Naixiang Li, Tonghai Liu\",\"doi\":\"10.1109/ICCET.2010.5486115\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present an approach for evolutionary learning of motif in biopolymer sequences. The focuses in this paper is evolutionary inference of Gaussian model, Differential Evolution for optimization and Markov chain Monte Carlo(MCMC) for sampling are applied in the probability learning of Gaussian model. The framework involves calculations of corresponding weight, mean and covariance. To obtain satisfied effect of MCMC sampling, the fitness function is discussed for MCMC ratio. Comparisons between results of Differential Evolution and Differential Evolution MCMC are provided to show novel effect of our method on synthetic dataset and real world dataset.\",\"PeriodicalId\":271757,\"journal\":{\"name\":\"2010 2nd International Conference on Computer Engineering and Technology\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 2nd International Conference on Computer Engineering and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCET.2010.5486115\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 2nd International Conference on Computer Engineering and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCET.2010.5486115","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Notice of RetractionEvolutionary learning of Gaussian model for motifs with differential evolution MCMC
In this paper, we present an approach for evolutionary learning of motif in biopolymer sequences. The focuses in this paper is evolutionary inference of Gaussian model, Differential Evolution for optimization and Markov chain Monte Carlo(MCMC) for sampling are applied in the probability learning of Gaussian model. The framework involves calculations of corresponding weight, mean and covariance. To obtain satisfied effect of MCMC sampling, the fitness function is discussed for MCMC ratio. Comparisons between results of Differential Evolution and Differential Evolution MCMC are provided to show novel effect of our method on synthetic dataset and real world dataset.