Forward-backward algorithm, used by watermark decoder for correcting non-binary synchronization errors, requires to traverse a very large scale trellis in order to achieve the proper posterior probability, leading to ...Forward-backward algorithm, used by watermark decoder for correcting non-binary synchronization errors, requires to traverse a very large scale trellis in order to achieve the proper posterior probability, leading to high computational complexity. In order to reduce the number of the states involved in the computation, an adaptive pruning method for the trellis is proposed. In this scheme, we prune the states which have the low forward-backward quantities below a carefully-chosen threshold. Thus, a wandering trellis with much less states is achieved, which contains most of the states with quite high probability. Simulation results reveal that, with the proper scaling factor, significant complexity reduction in the forward-backward algorithm is achieved at the expense of slight performance degradation.展开更多
Overfitting is one of the important problems that restrain the application of neural network. The traditional OBD (Optimal Brain Damage) algorithm can avoid overfitting effectively. But it needs to train the network r...Overfitting is one of the important problems that restrain the application of neural network. The traditional OBD (Optimal Brain Damage) algorithm can avoid overfitting effectively. But it needs to train the network repeatedly with low calculational efficiency. In this paper, the Marquardt algorithm is incorporated into the OBD algorithm and a new method for pruning network-the Dynamic Optimal Brain Damage (DOBD) is introduced. This algorithm simplifies a network and obtains good generalization through dynamically deleting weight parameters with low sensitivity that is defined as the change of error function value with respect to the change of weights. Also a simplified method is presented through which sensitivities can be calculated during training with a little computation. A rule to determine the lower limit of sensitivity for deleting the unnecessary weights and other control methods during pruning and training are introduced. The training course is analyzed theoretically and the reason why DOBD algorithm can obtain a much faster training speed than the OBD algorithm and avoid overfitting effectively is given.展开更多
[Objectives]This study was conducted to screen out reasonable pruning methods of walnut,and provide practical guidance for high-yield cultivation of walnut.[Methods]Xinfeng and Xinguang were used to study the effects ...[Objectives]This study was conducted to screen out reasonable pruning methods of walnut,and provide practical guidance for high-yield cultivation of walnut.[Methods]Xinfeng and Xinguang were used to study the effects of mechanical and artificial pruning methods on shoot growth,chlorophyll content in leaves,net photosynthetic rate and fruit quality.[Results]The results showed that:①the pruning method had a significant impact on the number of new shoots,and the number of new shoots of mechanical pruning was significantly higher than that of manual pruning;②the pruning method had a significant impact on the chlorophyll content,and the chlorophyll content of Xinguang of mechanical pruning was significantly higher than that of manual pruning;③the pruning method had a significant impact on the net photosynthetic rate of leaves,and the net photosynthetic rate of manual pruning was significantly higher than that of manual pruning;④Pruning methods had a significant effect on the number of walnut fruit,and the fruit trees pruned manually were significantly higher than those pruned mechanically;⑤the pruning method had no significant impact on the single fruit weight;⑥Pruning methods had a significant effect on the shell yield of a single walnut plant.The shell yield of single walnut plant pruned manually was significantly higher than that pruned mechanically;⑦pruning mode had a significant impact on the yield of walnut per plant,and the yield of artificially pruned walnut per plant was significantly higher than that of mechanical pruning;and⑧Xinfeng s chlorophyll content,net photosynthetic rate,fruit number,shell yield per plant and kernel yield per plant were all better than that of Xinguang,in addition,the growth and development of new shoots,the fruit quantity and quality of fruit also were affected by the interaction effect of genotype and pruning mode×variety.[Conclusions]It can be seen that different pruning methods have significant effects on the growth and development of new shoots and fruit yield and quality of walnut.Artificial pruning is more suitable for walnut cultivation by adjusting photosynthesis and improving the yield and quality of walnut;and Xinfeng is more suitable for popularization and production.展开更多
Ensemble techniques train a set of component classifiers and then combine their predictions to classify new patterns.Bagging is one of the most popular ensemble techniques for improving weak classifiers.However,it is ...Ensemble techniques train a set of component classifiers and then combine their predictions to classify new patterns.Bagging is one of the most popular ensemble techniques for improving weak classifiers.However,it is hard to deploy in many real applications because of the large memory requirement and high computation cost to store and vote the predictions of component classifiers.Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information,which has attracted a lot of attention from theory and application fields.In this paper,a novel rough sets based method is proposed to prune the classifiers obtained from bagging ensemble and select a subset of the component classifiers for aggregation.Experiment results show that the proposed method not only decreases the number of component classifiers but also obtains acceptable performance.展开更多
基金supported in part by National Natural Science Foundation of China (61101114, 61671324) the Program for New Century Excellent Talents in University (NCET-12-0401)
文摘Forward-backward algorithm, used by watermark decoder for correcting non-binary synchronization errors, requires to traverse a very large scale trellis in order to achieve the proper posterior probability, leading to high computational complexity. In order to reduce the number of the states involved in the computation, an adaptive pruning method for the trellis is proposed. In this scheme, we prune the states which have the low forward-backward quantities below a carefully-chosen threshold. Thus, a wandering trellis with much less states is achieved, which contains most of the states with quite high probability. Simulation results reveal that, with the proper scaling factor, significant complexity reduction in the forward-backward algorithm is achieved at the expense of slight performance degradation.
文摘Overfitting is one of the important problems that restrain the application of neural network. The traditional OBD (Optimal Brain Damage) algorithm can avoid overfitting effectively. But it needs to train the network repeatedly with low calculational efficiency. In this paper, the Marquardt algorithm is incorporated into the OBD algorithm and a new method for pruning network-the Dynamic Optimal Brain Damage (DOBD) is introduced. This algorithm simplifies a network and obtains good generalization through dynamically deleting weight parameters with low sensitivity that is defined as the change of error function value with respect to the change of weights. Also a simplified method is presented through which sensitivities can be calculated during training with a little computation. A rule to determine the lower limit of sensitivity for deleting the unnecessary weights and other control methods during pruning and training are introduced. The training course is analyzed theoretically and the reason why DOBD algorithm can obtain a much faster training speed than the OBD algorithm and avoid overfitting effectively is given.
基金the Project of Demonstration of Walnut Fine Variety Promotion and Standardized Management Technology in the Technology Promotion Demonstration Project of Central Finance Forest and Grass Science in 2020[Xin[2020]No.TG12].
文摘[Objectives]This study was conducted to screen out reasonable pruning methods of walnut,and provide practical guidance for high-yield cultivation of walnut.[Methods]Xinfeng and Xinguang were used to study the effects of mechanical and artificial pruning methods on shoot growth,chlorophyll content in leaves,net photosynthetic rate and fruit quality.[Results]The results showed that:①the pruning method had a significant impact on the number of new shoots,and the number of new shoots of mechanical pruning was significantly higher than that of manual pruning;②the pruning method had a significant impact on the chlorophyll content,and the chlorophyll content of Xinguang of mechanical pruning was significantly higher than that of manual pruning;③the pruning method had a significant impact on the net photosynthetic rate of leaves,and the net photosynthetic rate of manual pruning was significantly higher than that of manual pruning;④Pruning methods had a significant effect on the number of walnut fruit,and the fruit trees pruned manually were significantly higher than those pruned mechanically;⑤the pruning method had no significant impact on the single fruit weight;⑥Pruning methods had a significant effect on the shell yield of a single walnut plant.The shell yield of single walnut plant pruned manually was significantly higher than that pruned mechanically;⑦pruning mode had a significant impact on the yield of walnut per plant,and the yield of artificially pruned walnut per plant was significantly higher than that of mechanical pruning;and⑧Xinfeng s chlorophyll content,net photosynthetic rate,fruit number,shell yield per plant and kernel yield per plant were all better than that of Xinguang,in addition,the growth and development of new shoots,the fruit quantity and quality of fruit also were affected by the interaction effect of genotype and pruning mode×variety.[Conclusions]It can be seen that different pruning methods have significant effects on the growth and development of new shoots and fruit yield and quality of walnut.Artificial pruning is more suitable for walnut cultivation by adjusting photosynthesis and improving the yield and quality of walnut;and Xinfeng is more suitable for popularization and production.
基金Supported by the National Natural Science Foundation of China(Granted No.60775036 and No.60475019)the Ph.D.programs Foundation of Ministry of Education of China(No.20060247039)
文摘Ensemble techniques train a set of component classifiers and then combine their predictions to classify new patterns.Bagging is one of the most popular ensemble techniques for improving weak classifiers.However,it is hard to deploy in many real applications because of the large memory requirement and high computation cost to store and vote the predictions of component classifiers.Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information,which has attracted a lot of attention from theory and application fields.In this paper,a novel rough sets based method is proposed to prune the classifiers obtained from bagging ensemble and select a subset of the component classifiers for aggregation.Experiment results show that the proposed method not only decreases the number of component classifiers but also obtains acceptable performance.
