貝斯推理網路 的英文怎麼說

中文拼音 [bèisītuīlǐwǎnglù]
貝斯推理網路 英文
bayesian inference network
  • 貝 : 名詞1 [動物學] （蛤螺等有殼軟體動物的統稱） cowry; cowrie; shellfis 2 （古代用貝殼做的貨幣） cowr...
  • 斯 : Ⅰ名詞（古代驅疫時用的面具） an ancient maskⅡ形容詞[書面語] （醜陋） ugly
  • 推 : 動詞1 （向外用力使物體移動） push; shove 2 （磨或碾） turn a mill or grindstone; grind 3 （剪或削...
  • 理 : Ⅰ名詞1 （物質組織的條紋） texture; grain （in wood skin etc ） 2 （道理;事理） reason; logic; tru...
  • 網 : Ⅰ名詞1 （捕魚捉鳥的器具） net 2 （像網的東西） thing which looks like a net 3 （像網一樣的組織或...
  • 路 : 1 （道路） road; way; path 2 （路程） journey; distance 3 （途徑; 門路） way; means 4 （條理） se...
  • 貝斯 : alkemya: boubanebass boubane
  • 推理 : [邏輯學] inference; ratiocination; illation; reasoning; ratiocinate
  • 網路 : 1. [電學] network; electric network2. （網） meshwork; system; graph （指一維復形）; mesh
  1. Compared with the regular rule - based expert system, the bayesian network based es can reason on the incomplete input information using the prior probability distribution ; the topological structure of the network being used to express the qualitative knowledge and the probability distributions of the nodes in the network being used to express the uncertainty of the knowledge, which made the knowledge representation more intuitively and more clearly ; applying the principle of the bayesian chaining rule, bidirectional inference which allow infer from the cause to the effect and from the effect to the cause can be achieved

    與一般基於規則的專家系統相比，貝葉斯網專家系統利用先驗概率分佈，可以使推理在輸入數據不完備的基礎上進行；以網路的拓撲結構表達定性知識，以網路節點的概率分佈表達知識的不確定性，從而使不確定性知識的表達直觀、明確；利用貝葉斯法則的基本原理，可以實現由因到果及由果到因的雙向推理。
  2. Cbr systems have lots of strongpoints, such as the completely expressing of the information, the incrementally learning, the precisely simulating of the visualized thought, the conveniency of the obtaining knowledge, the h igh efficiency of solving new things and so on

    Cbr的顯著優點有：信息的完全表達，增量式學習，形象思維的準確模擬，知識獲取較為容易，求解效率高等。本論文研究了貝葉斯網路、範例推理以及貝葉斯網路在範例推理中的應用。
  3. At last, this thesis figures out an event - based method of air threat assessment through the definitions of the events, the modeling accompanied with xml description of the model, the introduction of the functional architecture model of event correlation, the type of event correlation and the expressions of the theory of this technique, the event deleting and contracting on the data facet, the correlation between the events in causality by bayesian network and the probability reasoning, exemplifying and calculating of bayesian network employed in the construction of threat assessment model of air battle

    最後提出了一種基於事件的空戰威脅估計方法。對事件進行了定義、建模並用xml語言進行了數據描述;介紹了事件關聯功能結構模型;介紹了事件關聯類型及知識表達方式，從數據層進行了事件清理和壓縮，使用貝葉斯網路對因果事件進行關聯，建立了空戰威脅估計貝葉斯網模型、進行了貝葉斯概率推理及算例分析。
  4. The two - stage modeling method takes into account the characteristics of software project risk management and software metrics data, integrates qualitative knowledge and quantitative data. to study the software project iterative process risk ’ s bayesian network model, the definition of cyclic bayesian network is presented, probability convergence property of directed cycle in cyclic bayesian network is proved and probability inference method is put forward

    論文在軟體項目迭代過程風險的貝葉斯網路模型研究中，定義了有環貝葉斯網路，證明了有環貝葉斯網路中有向環的概率收斂性質，給出了有環貝葉斯網路的概率推理方法。
  5. On uncertain inference based on the bayesian network

    基於貝葉斯網路不確定推理的研究
  6. While, study the scheduling bayesian network to model software project scheduling risk. the modeling method, related calculation and probability inference algorithm are presented

    在進度貝葉斯網路的研究中，給出了軟體項目進度風險的建模方法、模型中的相關計算以及概率推理演算法。
  7. In the paper, the models of uncertain reasoning are focused, such as the reasoning model of bayes probability, reliability theory, d - s evidence theory and neural network

    本文主要涉及的不確定推理模型包括主觀貝葉斯的概率推理模型，可信度理論推理模型，證據理論及其改進推理模型以及神經網路推理模型。
  8. Bayes network is a new inference and express method of uncertain knowledge

    摘要貝葉斯網路是不確定性知識表達與推理的一種新方法。
  9. 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。
  10. Classification has always been a central issue on data mining, machine learning and pattern recognition, classifier, as an important model and method of machine learning and data mining, is very important to the development and application of machine learning and the data mining. the classifier ’ s effect closely correlates with the characteristic of data sets, at present, the construction of classifier is generally based on the character of different datasets, there is no such a classifier which is suitable for any data sets. under uncertain conditions, the bayes network is a powerful tools for the knowledge expression and inference, but for difficulties in constructing its network structure and very high time complexity, it has not been considered as a classifier algorithm until the emergence of na ? ve - bayes classifier

    分類一直是數據挖掘、機器學習和模式識別等研究的核心問題，貝葉斯網路是作為知識表示和推理的強大工具，由於搜索空間巨大和學習困難的原因，直到樸素貝葉斯理論的出現才被作為分類器演算法，改進樸素貝葉斯分類器是貝葉斯分類器學習的一個主要的研究方向。遺傳演算法本質上是一種求解問題的高效并行全局搜索演算法，適合應用於那些改進的分類器的結構學習中。本文提出了一種基於遺傳演算法的ban分類器演算法。
  11. Bayesian inference network

    貝葉斯推理網路
  12. Research of exact inference algorithm in bayesian networks

    貝葉斯網路精確推理演算法的研究
  13. 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。
  14. Then the values of transcendent probabilities for all of the knowledge items are assigned and an overlay bayesian student model can be formed

    由於建立好的貝葉斯網路學生模型中存在無向環，使學生模型的推理成為一個np難題。
  15. Bayesian networks for causal reasoning in situation assessment

    用於態勢評估中因果推理的貝葉斯網路
  16. 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。
  17. Bn is a directed acyclic graph with network structure, which is intuitionistic and easy understanding. it can handle multi - information expression, data fusion and bi - directional parallel reasoning. the ability to colligate the prior information and the current information makes the inference much more accurate and believable

    貝葉斯網路是一種基於網路結構的有向圖解描述，具有多源信息一致表達與信息融合能力，能進行雙向并行推理，並能綜合先驗信息和樣本信息，使推理結果更為準確可信。
  18. Bayesian network ( bn ) is one of the most effective theoretical models for uncertainty knowledge expression and reasoning. it can be applied to decision with various dependent factors

    貝葉斯網路是目前不確定性知識表達和推理領域最有效的理論模型之一，適用於不確定性和概率性的知識表達和推理，特別適用於有條件地依賴多種控制因素的決策。
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