probability learning 中文意思是什麼

probability learning 解釋
機率學習
  • probability : n 1 或有；或然性。2 【哲學】蓋然性〈在 certainly 和 doubt 或 posibility 之間〉。3 【數學】幾率，...
  • 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...
  1. The artificial neural net ( ann ) way is universal regard as one of the most effective ways of stlf. in this paper, some research is developed for stlf using ann ways in several parts : the first part is about the arithmetic of ann based on bp model, namely the advanced of traditional bp arithmetic, one alterable step and scale bp arithmetic based on comparability of model and probability of accepting bp arithmetic is used to enhances a lot the convergence rate of learning process of bp network, but also avoid the stagnation problem to some extent. it indicates that the ann ' s efficiency and precision by the way can be ameliorated by the simulation of real data

    神經網路方法在短期預測中已經被公認為較有效的方法，本文針對神經網路用於電力系統短期負荷預測的幾個方面展開研究工作：第一部分研究一般用於負荷預測的神經網路bp模型的演算法，即對傳統的bp演算法的改進，將一種基於模式逼近度和接受概率的變步長快速bp演算法應用到短期負荷預測，模擬結果表明該方法有效的改善了bp演算法收斂速度慢以及容易陷入局部最小點的缺點，從而提高了神經網路用於負荷預測的效率和精度。
  2. 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。
  3. At last the learning method for conditional probability distribution is investigated. * the congestion computing of tn and simulation in this paper a special stochastic process is studied and applied in telephone < wp = 7 > switch system. the congestion principle is analyzed from link system, telecommunication network and switcher

    *電信網阻塞計算方法及模擬本文研究了增消隨機過程及在電話交換系統中的應用，並從鏈路系統、電信網路及交換機等方面分析了電信網產生阻塞的機理並推導了阻塞計算方法。
  4. We look at the problem of learning from examples as the problem of multivariate function approximation from sparse chosen data, and then consider the case in which the data are drawn, instead of chosen, according to a probability measure

    並檢視稀疏精選值中多變量函數近似法等這些從實例學習法所發現的問題，然後根據機率衡量，審思隨機獲得資料而非選定資料的案例。
  5. In the first chapter, the thesis illustrates the foundation and significance of this thesis and simply summarizes their researchful history and actualities of bn and cbr. in the second chapter, the thesis firstly explains the notion of bn, afterwards studies the application of bn in data - mining ( dm ) in detail and also studies the learning of the probability parameter and the structuring framwork of bn in the condition of the full data and the lacked data

    第一章，說明了本文的研究背景和意義並且簡單總結了貝葉斯網和範例推理的研究歷史和現狀。第二章，首先給出了貝葉斯網路的概念，然後詳細研究了貝葉斯網用於數據挖掘。分別對數據完整和不完整情況下，概率參數的學習和貝葉斯網結構的建立作了研究。
  6. Applying probability learning based evolutionary algorithm to parallel flow lines scheduling problem

    并行流程式生產線調度問題的概率分析求解演算法
  7. Bn is network structure with clarity semantics. lt exploits the structure of the domain to allow a compact representation of complex joint probability distribution. its sound probabilistic semantics, explicit encoding of relevance relationships, inference algorithms and learning algorithms that are fairly efficient and effective in pratice, and decision - making mechanism of facility, have led bn to enter the artificial intelligence ( ai ) mainstream. for the reasons that they have produced more and more practical values and economic profits in many important application fields, such as modern expert systems, diagnosis engines, decision support systems, and data mining systems, researchers from both industry and academia are thus taking them much seriously

    它具有清晰語義的網路結構；它揭示領域對象的內在結構，是復雜全概率分佈的緊湊表示方式；其堅實的理論基礎、知識結構的自然表述方式、靈活的推理能力、方便的決策機制及有效的學習能力使其成為一種主要的不確定知識的處理方法。貝葉斯網路已經在專家系統、決策支持系統、數據挖掘系統和範例推理系統等許多重要領域產生應用價值和經濟效益。
  8. Probability theory captures a number of essential characteristics of human cognition, including aspects of perception, reasoning, belief revision, and learning

    機率理論涵蓋了人類認知中的許多重要特徵，包括感知、推理、信念改變和學習方面。
  9. Parallel flow shop scheduling problem using probability learning based evolutionary algorithm

    并行流程車間調度問題及其概率學習進化演算法
  10. In the research of the algorithms and theory of temporal difference learning, a new class of multi - step learning prediction algorithms based on linear function approximators and recursive least squares methods is proposed, which are called the rls - td ( t ) learning algorithm. the convergence with probability one of the rls - td ( t ) algorithm is proved for ergodic markov chains, and the conditions for convergence are analyzed

    在時域差值學習（ temporaldifferencelearning ）學習演算法和理論方面，首次提出了一種基於線性值函數逼近的多步遞推最小二乘td （ ） （ rls - td （ ） ）學習演算法，並分析和證明了該演算法在求解遍歷markov鏈學習預測問題中的收斂條件和一致收斂性。
  11. 2. after expatiating the probability theoretic basis, it analyzes some issues including bn inference, and bn learning

    闡述了貝葉斯網路的概率理論基礎，分析了貝葉斯網路推理和貝葉斯網路學習等一般問題。
  12. This article discusses the probability and feasibility of digital libraries in support of e - learning

    摘要本文系探討數位典藏支援數位學習的契機及可行性。
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