推樹器 的英文怎麼說
中文拼音 [tuīshùqì]
推樹器
英文
tree stinger-
Thirdly, by introducing fuzzy theory into system evaluation, evaluating student, teaching, course resource, and function of whole system. fourthly, making use of learning from examples based on information theory, machine learning algorithm is improved and machine learning decision tree is realized. finally, on reasoning mechanism, combining means of two classes reasoning is taken
第三，在系統評價中引入了模糊理論，對學生、教學、課程資源以及系統的整體功能進行了評價；第四，採用基於信息論的示例學習，改進了決策樹學習演算法，並建立了機器學習決策樹；第五，在推理機制上，採取兩級推理相結合的方法進行推理，即用基於語義網路的模糊推理確定教學序列，用基於產生式規則的推理確定教學方法，並給出了詳細的推理演算法。Data warehouse is a hot research area in 90s its main motif is to provide the decision - maker a powerful tool : gathering the data in pure consistent, relevant pattern, and making use of the data in managing analyzing, data - mining purposec that means that the decision - maker can use the tool to understand, grasp the situation of the business from different directions and forecast the future of it when using data warehouse, the processing speed determines data warehouse ' s practicability and processing ability the hoc ( highway decision center ) system realized before solves some key problems about intermediate scale data, mainly concentrating data warehouse performance coefficient when using hdc in large scale data, it encountered processing speed problem then the settlement of this problem becomes a major research point so, based on the former research achievements, the present task is to construct the renowned data warehouse architecture and its relevant algorithms, then adapts the system to the large scale dataset with data mining functions c this paper is a part of the research in order to construct the powerful system, a key problem is to cope with the processing - speed problem and the data space problem, etc, - caused by the large scale dataset and magnificent dataset this is also the core in the present data mining research this paper ' s motive is to design and realize a decision - tree classifier in the data warehouse system for large - scale dataset
大型數據倉庫的處理速度問題目前是制約其推廣應用的關鍵所在，也是這一領域的一個重要研究課題，也正是我們當前工作的重點：在前期研究工作的基礎上圍繞提高大型數據倉庫處理速度問題，建立改進的數據倉庫系統模型和相關演算法，開發出面向中級以上企事業單位的、具有數據挖掘和分析能力的大型數據倉庫系統。建立大型數據倉庫所面臨的關鍵問題，是如何妥善解決實際業務數據的大規模、海量特徵所帶來的處理速度和空間等問題，這也是當前挖掘技術研究必然面對的核心問題。本研究的目的是設計並實現大型數據倉庫系統中的分類數據挖掘工具? ?決策樹分類器，主要工作是在綜合了解現有決策樹分類演算法的研究情況的前提下，對決策樹演算法適應大規模數據集的問題進行探討，力求設計出能較好地適應大規模數據的分類器演算法。Finally, conclusions are that the algorithm ' s efficency of utilization is higher, requires much less slots, and veracity is better than others. so it can resolve those collisions much better that multiple tags are transmitting data at the same time within reader ' s scope. it can promote rfid applications in more fields
最後總結得出結論：在解決碰撞過程中，動態二進制樹型搜索演算法與其他幾種演算法相比，可以使系統的吞吐率及通道的利用率更高、需要的時隙更少、準確率更高、能夠更好地解決了rfid系統中的在閱讀器作用范圍內有多個應答器應答要求通信而發生的碰撞問題，有助於推動射頻識別技術更廣泛的應用。
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