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Automatic Requirement Dependency Extraction Based on Integrated Active Learning Strategies
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作者 Hui Guan Guorong Cai Hang Xu 《Machine Intelligence Research》 EI CSCD 2024年第5期993-1010,共18页
Since requirement dependency extraction is a cognitively challenging and error-prone task,this paper proposes an automatic requirement dependency extraction method based on integrated active learning strategies.In thi... Since requirement dependency extraction is a cognitively challenging and error-prone task,this paper proposes an automatic requirement dependency extraction method based on integrated active learning strategies.In this paper,the coefficient of variation method was used to determine the corresponding weight of the impact factors from three different angles:uncertainty probability,text similarity difference degree and active learning variant prediction divergence degree.By combining the three factors with the proposed calculation formula to measure the information value of dependency pairs,the top K dependency pairs with the highest comprehensive evaluation value are selected as the optimal samples.As the optimal samples are continuously added into the initial training set,the performance of the active learning model using different dependency features for requirement dependency extraction is rapidly improved.Therefore,compared with other active learning strategies,a higher evaluation measure of requirement dependency extraction can be achieved by using the same number of samples.Finally,the proposed method using the PV-DM dependency feature improves the weight-F1 by 2.71%,the weight-recall by 2.45%,and the weight-precision by 2.64%in comparison with other strategies,saving approximately 46%of the labelled data compared with the machine learning approach. 展开更多
关键词 Requirement dependency dependency extraction dependency features integrated active learning strategies coefficient of variation
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Greedy feature replacement for online value function approximation
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作者 Feng-fei ZHAO Zheng QIN +2 位作者 Zhuo SHAO Jun FANG Bo-yan REN 《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2014年第3期223-231,共9页
Reinforcement learning(RL) in real-world problems requires function approximations that depend on selecting the appropriate feature representations. Representational expansion techniques can make linear approximators ... Reinforcement learning(RL) in real-world problems requires function approximations that depend on selecting the appropriate feature representations. Representational expansion techniques can make linear approximators represent value functions more effectively; however, most of these techniques function well only for low dimensional problems. In this paper, we present the greedy feature replacement(GFR), a novel online expansion technique, for value-based RL algorithms that use binary features. Given a simple initial representation, the feature representation is expanded incrementally. New feature dependencies are added automatically to the current representation and conjunctive features are used to replace current features greedily. The virtual temporal difference(TD) error is recorded for each conjunctive feature to judge whether the replacement can improve the approximation. Correctness guarantees and computational complexity analysis are provided for GFR. Experimental results in two domains show that GFR achieves much faster learning and has the capability to handle large-scale problems. 展开更多
关键词 Reinforcement learning Function approximation feature dependency Online expansion feature replacement
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Solving Topological and Geometrical Constraints in Bridge Feature Model
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作者 彭卫兵 宋亮亮 潘国帅 《Tsinghua Science and Technology》 SCIE EI CAS 2008年第S1期228-233,共6页
The capacity that computer can solve more complex design problem was gradually increased. Bridge designs need a breakthrough in the current development limitations, and then become more intelligent and integrated. Thi... The capacity that computer can solve more complex design problem was gradually increased. Bridge designs need a breakthrough in the current development limitations, and then become more intelligent and integrated. This paper proposes a new parametric and feature-based computer aided design (CAD) models which can represent families of bridge objects, includes knowledge representation, three-dimensional geometric topology relationships. The realization of a family member is found by solving first the geometric constraints, and then the topological constraints. From the geometric solution, constraint equations are constructed. Topology solution is developed by feature dependencies graph between bridge objects. Finally, feature parameters are proposed to drive bridge design with feature parameters. Results from our implementation show that the method can help to facilitate bridge design. 展开更多
关键词 bridge intelligent design feature mapping feature dependence graph constraint equations
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