learning by machine 中文意思是什麼

learning by machine 解釋
用機器學習
  • learning : n 學，學習；學問，學識；專門知識。 good at learning 善於學習。 a man of learning 學者。 New learn...
  • by : adv 1 在側，在旁，在附近。2 （擱）在一邊，（放）到旁邊，（存）在一旁；收著。3 （由旁邊）經過，過...
  • machine : n 1 機（器），機械；機關，機構。2 印刷機器；縫紉機；打字機；汽車；自行車；三輪車；飛機；〈美俚〉...
  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. By mapping input data into a high dimensional characteristic space in which an optimal separating hyperplane is built, svm presents a lot of advantages for resolving the small samples, nonlinear and high dimensional pattern recognition, as well as other machine - learning problems such as function fitting

    Svm的基本思想是通過非線性變換將輸入空間變換到一個高維空間，然後在這個新的空間中求取最優分類超平面。它在解決小樣本、非線性及高維模式識別問題中表現出許多特有的優勢，並能夠推廣應用到函數擬合等其他機器學習問題中。
  3. While network - based intrusion detection systems usually detect packet headers or part of those headers, this system examines the packet payload as well as headers. not only the attributes are detected respectively, but also the relationships of attributes occurrence are detected according to a set of conditional rules, which is generated automatically by a machine learning algorithm

    常見的基於網路的入侵檢測系統一般只對網路數據包的包頭或包頭中的某些內容進行考察，而本文實現的原型系統不僅考察數據包的包頭，而且還考察數據包應用負載的部分內容。
  4. Support vector machines ( svm ) are a kind of novel machine learning methods. it can solve small - sample learning problems better by using experiential risk minimization in place of structural risk minimination

    支持向量機（ supportvectormachines ，簡稱svm ）是在統計學習理論的基礎上發展起來的一種新的學習方法，它已初步表現出很多優于已有方法的性能。
  5. In recent years, reinforcement learning has become one of the key research areas in artificial intelligence and machine learning and it has attracted many researchers in other fields including operations research, control theory and robotics. reinforcement learning is different from supervised learning in that no teacher signals are needed and a reinforcement learning system learns by interacting with the environment to maximize the evaluative feedback from the environment

    增強學習與監督學習的不同之處在於，增強學習不要求給定各種狀態下的期望輸出即教師信號，而強調在與環境交互中的學習，以極大（或極小）化從環境獲得的評價性反饋信號為學習目標。
  6. Within the framework of sparse bayesian learning, the algorithm extends the relevance vector machine by combining global and local kernels adaptively in the form of multiple kernels, and the improved locality preserving projection ( llp ) is then applied to reduce the column dimension of the multiple kernel input matrix to achieve less training time

    在稀疏貝葉斯學習的框架下，該演算法首先以多核形式自適應結合全局核函數和局部核函數擴展相關向量機，然後應用改進的保局投影來約簡多核輸入矩陣的列維數以減少訓練時間。
  7. 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。
  8. Additionally, the optimum structure and parameters of the support vector machine can easily be determined by the learning process, however the neural networks can not. an information gain of signature signals is introduced to assess the contribution of the signature signals to diagnosing faults in rotating machines

    同時發現，存在一個最佳訓練樣本比例值，在該比例值上，不同核函數支持向量機的故障診斷錯誤率均趨于穩定，也就是說這個比例值確定了在保證故障診斷準確率的條件下，所需要的最少訓練樣本數。
  9. My thesis focuses on analysis network audit data using data mining, and mine rules about intrusion detection. intrusion detection efficiency is improved by using machine learning methods, thereby enhance adaptation of intrusion detection

    本文主要從數據挖掘的角度對網路數據進行分析，發掘出入侵檢測的模型，用機器學習的理論改進入侵檢測的效率，提高入侵檢測的自適應性。
  10. Suborninate node complete picking up equipment fault information and making machine learning arithmitics, and transmitting equipment condition information operated to principal node or the other subordinate node by the way of wireless data transmitting device, at the same time, it can receive the information of optimized model arithmetic and equipment condition parameter from principal node

    從節點內完成設備故障信息的提取和機器學習演算法，並將運算后的設備狀態信息通過無線數據傳裝置傳送到主節點或其它從節點，同時也可以接受來自主節點的優化建模演算法和設備狀態參數設置等信息。
  11. Because of ineffectiveness of naive bayes model for text classification, this thesis proposed integrating boosting theory of machine learning in classification process, boost naive bayes categorization model through many times training. improved by experiments, mutual information and naive bayes integrated with boosting bring very good precision, recall, and f1 score

    鑒于樸素貝葉斯的分類效果不佳，本論文又提出將機器學習中的boosting思想結合到樸素貝葉斯的分類模型中，對樸素貝葉斯模型進行提升，實驗證明，改進的互信息和給合了boosting思想的樸素貝葉斯分類模型均產生良好的分類效果?分準率、分全率及f1值。
  12. Expoles an algorithm about bearing surface defects detection by support vector machines that is the new branch of machine learning, in which the defective area and non - defective area are treated as two different textures and are sampled respectively to be learned, in order to reduce dimension, the image data can be processed by pca

    摘要提出一種基於支持向量機的軸承表面缺陷檢測演算法，該演算法把軸承中的非缺陷區域和缺陷區域分別看作兩種不同的紋理模式，利用主成分分析法（ pca ）對圖像進行降維處理，然後用支持向量機方法對兩類不同的樣本采樣學習，進行分類判斷。
  13. Unlike approach theory in orthodox statistics, statistical learning theory especially studies the law of machine learning when samples are finite. it has proved the bound of actual risk is made up of experiential risk and belief bound. vc dimension is used to control generation ability ; structural risk minimization induce principle is used to control the bound on the value of achieved risk by controlling experiential risk and belief bound at the same time

    不同於傳統統計學的漸進理論，統計學習專門研究有限樣本情況下的機器學習規律，它從理論上證明了實際風險的界是由經驗風險和置信范圍兩部分構成的，並給出了控制置信范圍的方法vc維。
  14. While, some algorithms of machine learning are introduced to get the intelligence of the individual of hfutagent which makes individual skills in the robocup. finally, we realize the multi - agent cooperation mechanism using the knoledge of soccer experts. in our system, a typical cooperation method in robocup called sbsp is used, and we explains how to use reinforcement learning method to reach the goal of local cooperation, and the offense and defense strategy system is build by decision - theoretic

    在本文中，首先介紹了典型的agent結構和mas模型和模擬機器人足球的一些主要模型：設計了一個分層的agent結構? hfutagent ，通過機器學習演算法實現了agent的個體智能；最後結合足球領域專家的知識實現了agent間的協作，其中使用了robocup中一個典型的協作方法- sbsp ，設計了一個通過強化學習的方法來達到agent之間的局部協作，把基於效用的對策論方法引入了hfutteam的進攻體系和防守體系中。
  15. They are agricultural productive materials price growth rate, sown area of grain crops growth rate, grain yield per area growth rate -, natural disaster covered grain areas growth rate, net grain import change rate, grain reserve change rate, population growth rate, per income growth rate, city and town population growth rate, food industry production value growth rate, year - end pig number growth rate, medical & pharmaceutical and textile industry production value growth rate, grain marketization degree, inflation rate using the previous year as base year ( preceding year = 100 ), public grain purchases price growth rate, investment in agricultural science and technology growth rate, investment in agricultural infrastructure growth rate, growth rate of graduates number from agriculture, forestry, science & technology universities and colleges and specialized secondary schools, government expenditure for agriculture and agricultural credit growth rate, international grain price growth rate, rmb exchange rate growth rate, last grain price growth rate, economic crop price growth rate, meanwhile, a new method is attempted to be used in this paper and the grain price early - warning problem is transformed into machine learning problem by introducing statistic learning theory and svm method which are gaining popularity in machine learning field at present in the world

    在此基礎上，篩選出23個警兆指標：農用生產資料價格增長率、糧食播種面積增長率、糧食單產增長率、糧食受災面積增長率、糧食凈進口量變化率、糧食儲備變動率、人口增長率、人均收入增長率、城鎮人口增長率、食品工業產值增長率、豬年末頭數增長率、醫藥紡織工業產值增長率、糧食市場化程度、以上年為基年的通貨膨脹率、國家糧食定購價格增長率、農業科技投入增長率、農業基礎設施投入增長率、農、林、科技高校大、中專畢業生人數增長率、財政支農資金比重及農業信貸增長率、國際糧食市場價格增長率、人民幣匯率增長率、上期糧食價格增長率、經濟作物價格增長率。同時論文在預警方法上作了新的嘗試，把糧食價格預警問題轉換成一個機器學習問題，引進當前國際上機器學習領域中比較熱門的統計學習理論和支持向量機方法，用順序回歸演算法對歷史數據進行學習建立了糧食價格預警模型。
  16. But the traditional search engine cannot meet people ' s demands on intelligent and personalized information service. so in this thesis, intelligent search engine is discussed, which can adjust itself to the user ' s interests and provides personalized information retrieval service. as a new solution, the information retrieval technique by natural language understanding and machine learning is presented in the thesis

    並就方案中涉及到的一系列理論和技術問題進行了研究，主要包括：提出了智能搜索引擎框架，在主動搜索和元搜索的基礎上增加了中文信息處理模塊，實現了搜索引擎的智能化和個性化服務；對漢語分詞技術進行了研究，綜合各種分詞方法實現了一套適合於智能搜索引擎系統的分詞系統。
  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. Abstract : by analyzing the method for evaluating the hidden danger in explosive factories ， based on the essential framework of self - learning of machine ， the process of implement the self - learning program with rote learning of such evaluation is discussed in detail

    文摘:通過對火炸藥工廠重大事故隱患危險性評估方法的分析，以計算機自學習的基本結構為主線，詳細探討了以機械學習策略完成該評估程序中對新危險品源自學習的過程。
  19. There are eight features used to form the feature vector for each sentence, and the summarizer is gained by machine learning algorithms, so automatic summarization is changed into classification task

    用這些特徵構成句子向量表示，並用機器學習的方法對其進行訓練得到摘要器，從而把自動文摘轉換為分類問題。
  20. Knowledge purification is the key procedure of knowledge acquisition, and machine learning is a effective method to gain wisdom for computers, among which artificial neural network with tutor coached can learn more accurate knowledge by faint structure, and then is a perfect way to deal with misty knowledge by describing and computing intangibly. lt is hard to describe or compute the misty relation of terms and document sort with accurate way. and we can figure out misty knowledge with misty way, so the paper introduces ann into vcm to form a conjoint method vcm ann

    。其中，有導師指導的人工神經網路能夠以模糊的結構學習較為精確的內容，是將模糊的知識進行模糊計算和模糊描述的理想方法。詞條項與文檔類別之間的模糊關系難以用精確的方法進行精確地描述與計算，模糊的知識用模糊的方法能得到較好的解決，因此本文將神經網路應用到信息檢索模型中，將之與向量空間模型相結合，形成了一種改進的向量空間模型vcmann 。
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