DocumentCode
518387
Title
Notice of Retraction
Evolutionary learning of Gaussian model for motifs with differential evolution MCMC
Author
Peng Guo ; Naixiang Li ; Tonghai Liu
Author_Institution
Sch. of Comput. Sci. & Technol., Tianjin Univ., Tianjin, China
Volume
1
fYear
2010
fDate
16-18 April 2010
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
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.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
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.
Keywords
Gaussian processes; Markov processes; Monte Carlo methods; evolutionary computation; learning (artificial intelligence); optimisation; sampling methods; Gaussian model; Markov chain Monte Carlo; Motifs; biopolymer sequences; differential evolution MCMC sampling; evolutionary inference; evolutionary learning; optimization; probability learning; real world dataset; synthetic dataset; Agricultural engineering; Computer science; DNA; Frequency; Parameter estimation; Pattern matching; Probability; Sampling methods; Search methods; Stochastic processes; Differential Evolution; Evolutionary Learning; Gaussian Model; MCMC;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5486115
Filename
5486115
Link To Document