機器學習系統 的英文怎麼說

中文拼音 [jīqìxuéxíxìtǒng]
機器學習系統 英文
machine learning system
  • 機 : machineengine
  • 器 : 名詞1. （器具） implement; utensil; ware 2. （器官） organ 3. （度量; 才能） capacity; talent 4. （姓氏） a surname
  • 學 : Ⅰ動詞1 （學習） study; learn 2 （模仿） imitate; mimic Ⅱ名詞1 （學問） learning; knowledge 2 （學...
  • 系 : 系動詞（打結; 扣） tie; fasten; do up; button up
  • 統 : Ⅰ名詞1 （事物間連續的關系） interconnected system 2 （衣服等的筒狀部分） any tube shaped part of ...
  • 機器 : 1. （用來轉換或利用機械能的機構） machine; machinery; engine 2. （引申為機構） apparatus; organ
  • 系統 : 1. （按一定關系組成的同類事物） system 2. （有條理的;有系統的） systematic
  1. During the procedure of system design and implementation, the author has made some innovative efforts such as : ( d establishing the user interest orientated model, the model receiving user interests continuously and conjecturing user interests by interaction with the user, accumulating user preferences in information demand, thereby achieving self - adaptive retrieval, ? roviding a feedback method which is based on the human - machine interaction, summarizing the user operations on the interface of result presentation, and designing an algorithm for capturing user operation behaviors, by which the changes in user interests and preferences can be learned potentially, ? ffering a method for user interest mining which can extract subjects of information confirmed by user, thereby conjecturing or predicting different kinds of expressions of the same interest or extracting the new interests or unexpressed interests, ? roposing a solution of personalized internet information retrieval based on the user interests in accordance with the above - mentioned work, the solution having very strong feasibility and practicality with taking user interest model as center, employing machine learning ( active learning and passive learning ) and data mining as tools, and being assisted with network robot,

    Piirs系統分析與設計過程中所做的創新性的嘗試主要有以下幾個方面：實現了基於用戶興趣的用戶模型，該模型通過與用戶的交互（主動交互和被動交互） ，不斷地接收用戶的興趣和推測用戶的興趣，積累用戶信息需求的偏好，實現自適應的檢索；提供了一種基於人機交互的反饋方法，對用戶在結果呈現界面上的操作進行了歸納總結，設計了用戶操作捕獲演算法， 「隱性地」學習用戶興趣和偏好的變化；提供了一種用戶需求挖掘的方法，對用戶已確定的信息做進一步的主題挖掘，由此推測或預測用戶同一興趣的不同表述方式或者挖掘出用戶新的或未表達出來的興趣；在上述工作基礎上提出了一套完整的基於用戶興趣的個性化網路信息檢索的解決方案，該方案以用戶興趣模型為中心，以機器學習（主動學習和被動學習）和數據挖掘為手段，輔以網路機器人，具有很強的可行性和實用性。
  2. A double - bagging machine - learning algorithm was used to train classification rules on the basis of a combination of fdt scores and nerve fiber related visual field losses in swap

    在聯合fdt評分和swap神經纖維相關的視野缺損基礎上用雙相機器學習系統排列分類法則。
  3. Data mining merges many important research fields including machine learning, artificial intelligent, statistics, knowledge - base systems and data visualization, etc. however, current algorithms proposed for date mining of association rules require several passes over the analyzed database

    挖掘重要數據仍然需要配合許多其他領域的技術才能得到完善有效的結果，其中包括機器學習，人工智慧，統計學原理，數據庫系統，數據可視化等。
  4. 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。
  5. An experienced human operator may have little knowledge about a complex system but can still doing good job in system control and fault diagnosis by observing signals of inputs and outputs. therefore, the problem is, can we use some techniques of machine learning and artificial intelligent to mimic the human ability of " learn to control "

    在實際工業生產中，工程技術人員在對系統機理和數學模型知之甚少的情況下通過觀察和經驗總結，仍然能夠對系統進行良好的手動控制和及時的故障診斷，因此完全可以採用機器學習的智能方法模擬人的這種通過觀察學習進行控制的能力。
  6. Firstly, the backgrounds and techniques of intrusion detection system ( ids ) are introduced briefly in this paper, also the current status. then an intrusion detection system based on a new nonstationary machine learning technique model is presented and improved. on the precondition of the same false alarm rates, more intrusions have been detected, and less time for detection is needed with the improved system

    本文首先引入了一種基於機器學習規則的、用於入侵檢測系統的動態模型，並對該模型加以改進，在誤警率不變的前提下，使系統能夠檢測到更多的入侵種類，縮短了系統檢測時間，從而提高了系統性能。
  7. With dramatic advance and wide application of the internet and information systems, we can easily attain large quantities of data that is also in rapid increment daily. thereby it becomes impractical to handle these data manually. we wish that computers can automatically process these data and extract potentially useful knowledge from them to help us arrange managements and make decisions

    隨著網際網路和信息系統的迅猛發展和廣泛應用，人們可以輕易地獲得海量的數據，並且這些數據還在日益不斷地增長中，對這樣的數據仍然採用傳統的人工處理方法變得不切實際，於是希望計算機能夠自動地幫助我們處理這些海量的數據，並提取出隱含的有價值的知識，輔助管理和決策，這就需要研究者對機器學習，特別是數據庫知識發現作更加深入和廣泛的研究。
  8. The main work at the beginning of 1970 ' s was to construct the machine learning system that can simulate human ' s concept acquision

    第二階段是20世紀70年代初。主要工作是建立各種模擬人的概念學習的機器學習系統。
  9. We had always known that our machine - learning systems, which focus on the words in a message, would be vulnerable to spammers who obscure the wording of their output

    我們一向知道，我們的機器學習系統是以訊息內的文字做為判斷基準，因此垃圾郵件寄件者若對訊息內的文字動手腳，就可騙過系統。
  10. Spammers sometimes hide their message in an image, where machine - learning systems cannot analyze the content ( although they can still exploit other clues, such as the links in the message, sender reputation information, and so forth )

    垃圾郵件寄件者有時會把訊息藏在影像裏面，讓機器學習系統無法分析訊息的內容（但系統仍可利用其他線索，比如超連結以及寄件者的評價訊息等等） 。
  11. In this paper, the upper - class intelligent control scheme is adopted according to the characteristics of tobacco re - dryer and the present control condition in order to solve the problem that the output tobacco moisture can ? keep stable. that is, to keep the old every pid control link, to add the upper - class computer so that sets up the upper - class intelligent control system that consists of the description management sub - system, the fuzzy compensation on line sub - system and the self - learning system. the following are control procedure : first of all, a series of output values of description can be obtained by the forward inference of basic controller on the basis of the information of input tobacco, then, these output values can be adjusted by the fuzzy compensation controller with inquiring the fuzzy control - list on line in the light of the output tobacco moisture error

    本文針對烤機的工藝特點和目前控制現狀，提出用上位智能控制的方法解決烤機目前存在的問題，即在保留原有各段pid控制的基礎上，增加上位計算機，建立起由配方管理子系統、模糊在線修正子系統和自學習系統組成的上位計算機監控、決策的智能控制系統；控制過程為：基本配方控制器根據來料信息正向推理提供煙葉復烤機各段參數設定值的基值；模糊補償控制器根據煙葉水分偏差在線查詢模糊控製表提供相應參數設定值的修正值，實現煙葉復烤機參數的在線修正；學習環節的引入實現煙葉配方的在線優化。
  12. The fundamental theoretical framework of this dissertation is the " stimulus - response " model of psychological behaviorism. the author applies this model into the apparel - buying behavior and proposes the " systematical integrated model " in the apparel consumption decision - making. the author tends to find out the correspondent relationship between the different buying behaviors and their characteristics through the newest technique of data mining - - hybrid machine learning ( hml ). therefore, three sub - models on apparel consumption will be established

    本研究的立足點是心理學行為主義學派「刺激? ?反應」理論在服裝消費行為決策過程中的延伸，並在購買者行為模式的基礎上提出了服裝消費決策行為系統整合模型，筆者試圖通過系統整合的思想，採用數據挖掘的最新技術? ?混合機器學習（ hybridmachinelearning ，簡稱hml ）力圖找出具有不同購買反應的人群特徵的對應關系，即通過hml對所採集的數據庫的運行及其分析，構建上海地區服裝消費在「購買產品及其數量選擇」 、 「購買地點選擇」和「購買者確定」三個子模型，從而勾勒上海家庭服裝消費決策行為系統整合模型。
  13. The dissertation mainly aims at applying several active machine learning strategies to intrusion detection and systematically studies signal analysis techniques of intrusion detection based on statistical learning theory ( slt ), symbol inductive learning theory and genetic learning method. meanwhile, performance comparison and evaluation among intrusion detection techniques based on different machine learning strategies are presented according to computational learning theory and statistical hypothesis test methodology. intrusion detection is regarded as a pattern recognition problem in term of statistical learning theory ; i

    本文的主要工作是將目前幾種有生命力的機器學習策略應用於入侵檢測技術中，論文從入侵檢測的不同視角出發，系統深入地研究了統計學習理論、基於符號的歸納學習理論和遺傳學習方法在入侵檢測信號分析中的應用技術，並在可能近似正確（ pac ）學習框架下，利用計算學習理論和統計假設檢驗方法對基於不同機器學習策略的入侵檢測方法進行了性能比較和評估。
  14. Image processing system is a very important part of mini unmanned helicopter, which mainly dealing with how to detect the object and supply the navigation computer with location information of the object. however, the varied background and the moving of the camera make the image processing become difficult. to solve these problems, this thesis introduces an object detection algorithm based on contour retrieving, template match and hu moments invariant

    硬體組成詳細探討了系統結構，並對所採用的ccd攝像機、視頻採集卡等硬體單元作了描述；在軟體設計中，首先對不同操作系統環境下的視頻捕獲做了討論；接著，重點研究了針對機載攝像機拍攝圖像的目標檢測方法，提出並實現了基於模板匹配和不變矩的目標檢測演算法，以及基於機器學習的目標檢測演算法。
  15. This article brings forward a new kind of machine - learning method ? extension machine method. in this method, according to the evaluation of the model in the system utilizing extension evaluation method, the model piles up experience in the continually us ing and obtains the exact knowledge about decision, corrects its parameter and ameliorate the arithmetic of itself. in this way, the model achieve the aim of perfecting its capability through self - learning

    本文提出了一種新的機器學習方法? ?可拓機器方法，利用可拓評價方法對系統中的模型進行評估，根據評估結果，使模型在不斷使用過程中積累經驗，獲取與決策相關的正確知識，修正模型參數，改進模型演算法，從而使模型通過自我學習，達到逐步完善自身性能的目的。
  16. The article introduced agent machine learning chinese word segmentation. generally, focused on over agent ' s intelligibility, substitution, go - aheadism

    文章系統介紹了智能代理，機器學習和漢語分詞技術，其中著重研究了agent的智能性，代理性，主動性。
  17. 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。
  18. First, n - degree of freedom robotic manipulator control problem is studied. on the basis of computed torque ( ct ), the uncertainty is learned by the neural network, and the output of the neural network is used as compensator. this approach overcome the deterioration of control performance due to the uncertainties including working circumstance and load change

    針對計算力矩法對模型誤差敏感，魯棒性差的特點，文中將神經網路和計算力矩方法結合，首先根據標稱模型設計計算力矩控制器，然後採用神經網路來學習系統中的不確定性，神經網路的輸出作為補償控制器：有效的克服了機械手由於工作環境和負載變化等不確定性引起的控製品質的破壞。
  19. A frame representation was chosen to express the knowledge of carqs, and c + + was used to define the data structure. aiming at the design objective of system, rote learning was chosen to improve the performance. chapter four discussed how the dijkstra ' s arithmetic was ameliorated

    在分別對知識表示、機器學習研究探討的基礎上，經過對多種知識表示方法進行分析、比較，最終確定利用框架表示法表示carqs中的知識，用c + +語言對數據結構進行了定義，並針對系統設計目標，選擇機械學習作為性能改進途徑。
  20. Neural network ensemble can significantly improve the generalization ability of learning systems through training a finite number of neural networks and then combining their results. it is not only helpful for experts to investigate machine learning and neural computing but also helpful for engineers to solve real world problems using neural network techniques

    神經網路集成通過訓練多個網路並將其結論進行合成，可以顯著地提高學習系統的推廣能力，它不僅有助於專家對機器學習和神經網路的深入研究，還有助於工程技術人員利用神經網路技術來解決現實世界中的問題。
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