[目的]筛选适宜常熟地区穴盘丝瓜幼苗徒长的抑制方法,为集约化穴盘育苗提供理论和实践依据。[方法]以春语微生物菌剂和不同浓度的矮壮素分别对连栋大棚日光温室内丝瓜育苗基质进行底水浇灌处理,研究不同处理对工厂化丝瓜幼苗徒长调控的...[目的]筛选适宜常熟地区穴盘丝瓜幼苗徒长的抑制方法,为集约化穴盘育苗提供理论和实践依据。[方法]以春语微生物菌剂和不同浓度的矮壮素分别对连栋大棚日光温室内丝瓜育苗基质进行底水浇灌处理,研究不同处理对工厂化丝瓜幼苗徒长调控的作用。[结果]播种前用稀释100倍的春语微生物菌剂和0.2 m L/L矮壮素溶液以浇透底水的方式处理的丝瓜幼苗壮苗效果最为显著,与对照相比,SPAD值显著增加,株高矮化显著,茎粗增加明显,根冠比和壮苗指数也显著升高。[结论]在播种前用稀释100倍的春语微生物菌剂和0.2 m L/L矮壮素溶液浇透育苗基质的方法来控制丝瓜幼苗的徒长,达到壮苗的目的。展开更多
In machine learning,randomness is a crucial factor in the success of ensemble learning,and it can be injected into tree-based ensembles by rotating the feature space.However,it is a common practice to rotate the featu...In machine learning,randomness is a crucial factor in the success of ensemble learning,and it can be injected into tree-based ensembles by rotating the feature space.However,it is a common practice to rotate the feature space randomly.Thus,a large number of trees are required to ensure the performance of the ensemble model.This random rotation method is theoretically feasible,but it requires massive computing resources,potentially restricting its applications.A multimodal genetic algorithm based rotation forest(MGARF)algorithm is proposed in this paper to solve this problem.It is a tree-based ensemble learning algorithm for classification,taking advantage of the characteristic of trees to inject randomness by feature rotation.However,this algorithm attempts to select a subset of more diverse and accurate base learners using the multimodal optimization method.The classification accuracy of the proposed MGARF algorithm was evaluated by comparing it with the original random forest and random rotation ensemble methods on 23 UCI classification datasets.Experimental results show that the MGARF method outperforms the other methods,and the number of base learners in MGARF models is much fewer.展开更多
文摘[目的]筛选适宜常熟地区穴盘丝瓜幼苗徒长的抑制方法,为集约化穴盘育苗提供理论和实践依据。[方法]以春语微生物菌剂和不同浓度的矮壮素分别对连栋大棚日光温室内丝瓜育苗基质进行底水浇灌处理,研究不同处理对工厂化丝瓜幼苗徒长调控的作用。[结果]播种前用稀释100倍的春语微生物菌剂和0.2 m L/L矮壮素溶液以浇透底水的方式处理的丝瓜幼苗壮苗效果最为显著,与对照相比,SPAD值显著增加,株高矮化显著,茎粗增加明显,根冠比和壮苗指数也显著升高。[结论]在播种前用稀释100倍的春语微生物菌剂和0.2 m L/L矮壮素溶液浇透育苗基质的方法来控制丝瓜幼苗的徒长,达到壮苗的目的。
基金Project(61603274)supported by the National Natural Science Foundation of ChinaProject(2017KJ249)supported by the Research Project of Tianjin Municipal Education Commission,China。
文摘In machine learning,randomness is a crucial factor in the success of ensemble learning,and it can be injected into tree-based ensembles by rotating the feature space.However,it is a common practice to rotate the feature space randomly.Thus,a large number of trees are required to ensure the performance of the ensemble model.This random rotation method is theoretically feasible,but it requires massive computing resources,potentially restricting its applications.A multimodal genetic algorithm based rotation forest(MGARF)algorithm is proposed in this paper to solve this problem.It is a tree-based ensemble learning algorithm for classification,taking advantage of the characteristic of trees to inject randomness by feature rotation.However,this algorithm attempts to select a subset of more diverse and accurate base learners using the multimodal optimization method.The classification accuracy of the proposed MGARF algorithm was evaluated by comparing it with the original random forest and random rotation ensemble methods on 23 UCI classification datasets.Experimental results show that the MGARF method outperforms the other methods,and the number of base learners in MGARF models is much fewer.
