decision tree learning 中文意思是什麼
decision tree learning
解釋
決策樹-
Developing from the typical decision tree learning system ids and using the idea of weight sum, we did exploring research on the application of decision tree technology, using weight entropy to get predicted attribute and division value, in crm of tourism industry
在典型的決策樹學習系統id3之上，利用「加權和」的思想，在對決策樹技術（使用加權熵來獲得預測屬性和分裂值）在旅遊crm中應用上作了探索性研究。 -
Network forensics is an important extension to present security infrastructure, and is becoming the research focus of forensic investigators and network security researchers. however many challenges still exist in conducting network forensics : the sheer amount of data generated by the network ; the comprehensibility of evidences extracted from collected data ; the efficiency of evidence analysis methods, etc. against above challenges, by taking the advantage of both the great learning capability and the comprehensibility of the analyzed results of decision tree technology and fuzzy logic, the researcher develops a fuzzy decision tree based network forensics system to aid an investigator in analyzing computer crime in network environments and automatically extract digital evidence. at the end of the paper, the experimental comparison results between our proposed method and other popular methods are presented. experimental results show that the system can classify most kinds of events ( 91. 16 ? correct classification rate on average ), provide analyzed and comprehensible information for a forensic expert and automate or semi - automate the process of forensic analysis
網路取證是對現有網路安全體系的必要擴展，已日益成為研究的重點.但目前在進行網路取證時仍存在很多挑戰:如網路產生的海量數據;從已收集數據中提取的證據的可理解性;證據分析方法的有效性等.針對上述問題，利用模糊決策樹技術強大的學習能力及其分析結果的易理解性，開發了一種基於模糊決策樹的網路取證分析系統，以協助網路取證人員在網路環境下對計算機犯罪事件進行取證分析.給出了該方法的實驗結果以及與現有方法的對照分析結果.實驗結果表明，該系統可以對大多數網路事件進行識別（平均正確分類率為91 . 16 ? ） ，能為網路取證人員提供可理解的信息，協助取證人員進行快速高效的證據分析 -
Just as most of the natural language process technologies, the methods of ner have two classes, statistic - based and rule - based. considering of the limitation of using only one of the methods, we combined both of the methods to recognize named entity in this thesis. we combined the maching learning with ner to make the system get the ability of self - learning. we have done research on decision tree of maching learning mainly and designed a recognize model to recognize named entity. this model first used the probability and statistic way to extract the potential named entities, and then some context linguistic language information are employed in the model to recognize the named entities furtherly. as the wrong entites are denied, the recongnize effect has been improved
鑒于單獨採用基於統計方法或基於規則方法的缺陷，在這篇論文中，採用了統計與規則相結合的方法來識別命名實體。為了使系統具有學習能力，我們把機器學習方法應用於中文命名實體的識別，這里我們著重研究了機器學習中的決策樹方法在中文命名實體識別中的應用;設計了一種基於決策樹的識別模式，該模式首先利用概率統計方法，在文本中盡量完備地識別出潛在的命名實體，然後利用潛在命名實體相關的上下文詞法、語法和語義特徵作為屬性構建決策樹，否定不正確的實體，進一步提高了命名實體識別的準確率。 -
Hybrid machine learning ( hml ) is the latest applying in the field of intelligent information process. it combines the induced learning based - on decision - making tree with the blocking neural network. and it provides a useful intelligent knowledge - based data mining technique
混合機器學習是在智能信息處理上的最新應用，它把以決策樹為基礎的歸納學習與模塊化的神經網路演算法結合起來，從而提供了一種在知識基礎上進行證實和確認的行之有效的智能化數據挖掘過程，其核心演算法是id3和ftart 。 -
The second chapter mainly analyzes data mining. select decision tree method to realize after learning data mining concept, model and classification
首先介紹了數據挖掘技術的概念、模型以及分類，經過分析，決定選取決策樹方法來具體實現。 -
Data mining is defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data or known as knowledge - discovery in databases ( kdd ). to do this, data mining uses computational techniques from statistics, machine learning and pattern recognition such as discriminate analysis, regression method, mathematical programming, decision tree, k - nearest neighbor, artificial neural network etc. although many positive attempts are done, the development and application of personal credit assessment model in chinese bank industry is still in its infancy
數據挖掘是20世紀90年代後期人工智慧和數據庫領域興起的一種數據處理和知識發現（ kdd ）理論，是從大量的、不完全的、有噪聲的、模糊的和隨機的實際應用數據中，提取隱含在其中的信息和知識的過程。對數據進行分類和預測是數據挖掘的主要功能。數據挖掘用於信用評估的優勢主要在於: （ 1 ）能處理和修正實際數據問題，演算法模型具有自檢 -
In the execution process of data mining, the author reduces the dimensions with the method of neural network learning and produce rule sets with the method of decision tree learning
在實施數據挖掘過程中，根據神經網路和決策樹方法各自固有的優點，將神經網路運用於屬性的規約，而將決策樹用於產生規則模型。 -
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。 -
In our research, variation of contour line is used for describing the characteristics of image structure. using teacher images and machine learning method, an image direction classification model is built as a decision tree. test results argued the validity of this method
本課題使用圖像輪廓線向量特徵來反映圖像的構圖特徵，通過教師實例獲得用戶的方向分類概念，然後利用決策樹的學習建立分類模型，並且在此分類模型的基礎上實現了一個圖像正立方向判別系統。 -
Induction learning of decision tree based on id3 algorithm is an important branch of inductive learning now, which can be used to automatic acquisition of knowledge
基於id3演算法的決策樹歸納學習是歸納學習的一個重要分支，可用於知識的自動獲取過程。 -
With the studying of decision tree, the decision tree learning that only can describe the accurate characters is not adapted to the requirement describing the imprecise knowledge of a system. the world human being belong to and the domains issues within are dynamic. so in the real world, we hope the decision tree is capable of describing the dynamic fuzzy issues
隨著決策樹歸納學習研究的深入，具有精確描述特徵的決策樹歸納學習已經不能適應一個系統中不精確的知識表達的要求，同時由於人們所處的世界和問題所在的域都是時刻運動變化的，所以在現實問題中人們更希望決策樹能夠描述具有動態模糊性的問題。
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