approximate procedure 中文意思是什麼
approximate procedure
解釋
近似程序- approximate : vt 1 使接近。2 接近；走近。3 近似，約計。4 模擬。5 估計。vi 近於。 His income this year approxima...
- procedure : n. 1. 工序，過程，步驟。2. 程序，手續；方法；訴訟程序；（議會的）議事程序。3. 行為，行動，傳統的做法；（外交、軍隊等的）禮儀，禮節。4. 〈罕用語〉進行。
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Optimized association rules are permitted to contain uninstantiated attributes. the optimization procedure is to determine the instantiations such that some measures of the roles are maximized. this paper tries to maximize interest to find more interesting rules. on the other hand, the approach permits the optimized association rule to contain uninstantiated numeric attributes in both the antecedence and the consequence. a naive algorithm of finding such optimized rules can be got by a straightforward extension of the algorithm for only one numeric attribute. unfortunately, that results in a poor performance. a heuristic algorithm that finds the approximate optimal rules is proposed to improve the performance. the experiments with the synthetic data sets show the advantages of interest over confidence on finding interesting rules with two attributes. the experiments with real data set show the approximate linear scalability and good accuracy of the algorithm
優化關聯規則允許在規則中包含未初始化的屬性.優化過程就是確定對這些屬性進行初始化，使得某些度量最大化.最大化興趣度因子用來發現更加有趣的規則;另一方面，允許優化規則在前提和結果中各包含一個未初始化的數值屬性.對那些處理一個數值屬性的演算法進行直接的擴展，可以得到一個發現這種優化規則的簡單演算法.然而這種方法的性能很差，因此，為了改善性能，提出一種啟發式方法，它發現的是近似最優的規則.在人造數據集上的實驗結果表明，當優化規則包含兩個數值屬性時，優化興趣度因子得到的規則比優化可信度得到的規則更有趣.在真實數據集上的實驗結果表明，該演算法具有近似線性的可擴展性和較好的精度 -
The pheromone - based parameterized probabilistic model for the aco algorithm is presented as the solution construction graph that the combinatorial optimization problem can be mapped on. based on the solution construction graph, the unified framework of the aco algorithm is presented. an iterative update procedure of the solutions distribution in the problem ' s probabilistic model is proposed, that will converge to the optimal solutions with probability one, then the minimum cross - entropy pheromone update rule is proposed to approximate the iterative update procedure by minimizing the cross - entropy distance and monte - carlo sampling
基於解空間參數化概率分佈模型，首先提出了一個以概率1收斂于最優解的解空間概率分佈的迭代更新過程，然後提出了通過最小化不同分佈間的交互熵距離以及蒙特卡洛采樣來逼近此迭代過程的最小交互熵信息素更新規則，接著分別給出了弧模式以及結點模式信息素分佈模型下的最小交互熵等式。
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