An ensemble learning algorithm based on game theory is proposed to evaluate algorithms of image analysis and image feature extraction.A competition system is established to implement the algorithm for evaluating the a...An ensemble learning algorithm based on game theory is proposed to evaluate algorithms of image analysis and image feature extraction.A competition system is established to implement the algorithm for evaluating the applicability and efficiency of different edge detection algorithms.Through the game in the algorithm competition system,the most suitable algorithm as a winner in the competition can be selected.A group of optimal parameters for the corresponding edge detection can also be found.Firstly,based on the evolutionary game theory,a strategy of the competition of edge extraction algorithms is developed.Secondly,after selecting the most suitable algorithm from the candidates,the overall parameters are optimized.Experiments show that for a specific class of images,several candidate algorithms can be used as a class of preference algorithms based on the final evolutionary result.When analyzing the images,the priority algorithm can be recommended as the best edge detection algorithm from these reference algorithms.It is more effective than traditional methods in determining an algorithm and choosing parameters.展开更多
Enhanced Data Rates for Global System for MobileCommunication (GSM) Evolution (EDGE) is a controversialtechnology for telecom operators. Basedon an actual network testing and analyzing, the paperdeals with the perform...Enhanced Data Rates for Global System for MobileCommunication (GSM) Evolution (EDGE) is a controversialtechnology for telecom operators. Basedon an actual network testing and analyzing, the paperdeals with the performance and actual impacts of thecompleted EDGE projects mainly through comparisonapproaches.展开更多
基金supported by the National Key Research and Development Program of China (2016YB0700502, 2018YFB0704400)the National Natural Science Foundation of China (51532006)
文摘An ensemble learning algorithm based on game theory is proposed to evaluate algorithms of image analysis and image feature extraction.A competition system is established to implement the algorithm for evaluating the applicability and efficiency of different edge detection algorithms.Through the game in the algorithm competition system,the most suitable algorithm as a winner in the competition can be selected.A group of optimal parameters for the corresponding edge detection can also be found.Firstly,based on the evolutionary game theory,a strategy of the competition of edge extraction algorithms is developed.Secondly,after selecting the most suitable algorithm from the candidates,the overall parameters are optimized.Experiments show that for a specific class of images,several candidate algorithms can be used as a class of preference algorithms based on the final evolutionary result.When analyzing the images,the priority algorithm can be recommended as the best edge detection algorithm from these reference algorithms.It is more effective than traditional methods in determining an algorithm and choosing parameters.
文摘Enhanced Data Rates for Global System for MobileCommunication (GSM) Evolution (EDGE) is a controversialtechnology for telecom operators. Basedon an actual network testing and analyzing, the paperdeals with the performance and actual impacts of thecompleted EDGE projects mainly through comparisonapproaches.
