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Recent studies show structured atomic event information is beneficial to represent the discourse semantic. However, extracting useful structured representation of events from open domain is a challenging problem. On one hand, previous event extraction methods on special domain, cannot be directly used for open domain because of domain limitation and predefined event pattern. On the other hand, atomic...
This paper presents a novel machine learning model-kernel granular support vector machine (KGSVM), which combines traditional support vector machine (SVM) with granular computing theory. By dividing granules and replacing with them in kernel space, the datasets can be reduced effectively without changing data distribution. And then the generalization performance and training efficiency of SVM can...
This paper considers the problem of output feedback passive control for delayed systems with time-varying delay. We develop a dynamic output feedback controller design method and propose such controllers that guarantee the closed-loop system be strongly stable with strictly passive. Sufficient conditions for the solvability of the problem are derived in terms of linear matrix inequalities (LMIs)....
This paper deals with the problem of output feedback robustly passive control for parameter uncertain systems with time-varying delay. The parameter uncertainties are norm-bounded. We provide a dynamic output feedback controller that guarantees the closed-loop systems strongly robustly stable with strictly passive for all admissible uncertainties. Delay-dependent sufficient conditions for the existence...
FP-tree is an efficient algorithm for generating frequent item-sets and the present algorithms are all based on FP_tree generally. But the FP_treepsilas generative process needs much time and it needs to scan database twice. In order to improve the efficiency of constructing the FP_tree, a new fast algorithm called Level FP_tree (abbreviate L-FP_tree) was proposed. The algorithm contains two main...
The main challenge of mining sequential patterns is the high processing cost of support counting for large amount of candidate patterns, and a lot of patterns are not interesting to users. In this paper, a novel algorithm MSMA (maximal sequential pattern mining based on simultaneous monotone and anti-monotone constraints) incorporating both maximal and constraint-based sequential pattern mining in...
Recently, most of the studies on mining frequent patterns focus on improving the efficiency of frequent itemtsets generations, but the I/O cost of database scanning has been a bottle-neck problem in data mining. Many algorithms proposed recently are based on apriori and FP tree, and the FP growth algorithm based on FP tree is more efficient than Apriori because the candidates are not generated. But...
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