probabilistic reasoning 中文意思是什麼

probabilistic reasoning 解釋
概率式推理
  • probabilistic : adj. 1. （天主教教義）蓋然論的，或然說的。2. 概率的，幾率的。
  • reasoning : n. 推論，推理；論究，論斷；理論，論證；論法。adj. 能推理的；有關推理的。 the reasoning power 推理力。 a reasoning creature 理性動物，人類。
  1. In 1991, he introduced the concept of soft computing, the principal constituents of which are fuzzy logic, neural network theory and probabilistic reasoning

    一九九一年澤德教授提出軟計算的概念，內容主要包括快思邏輯、神經網路理論及概率推理。
  2. ( 5 ) a series of design methods of classifiers are proposed, including the classifier based on the generalized inverse and the probabilistic reasoning method ( prm ), a new self - adaptive kohonen clustering network which overcomes the shortcomings of the conventional clustering algorithms, and the fuzzy neural classifier. the experimental study efface recognition is presented based on the combination of multi - feature multi - classifier. ( 6 ) this paper proposes a hybrid feature extraction method for face recognition, which is a combination of the eigen matrix, fisher discriminant analysis, and the generalized optimal set of discriminant vectors

    （ 5 ）對圖象分類器設計方法進行研究，主要包括：提出了一種基於廣義逆和概率推理的分類器設計方法；提出了一種新的自適應模糊聚類演算法；提出了基於模糊神經網路的分類器設計方法；並對多特徵多分類器組合方法在人臉識別中進行實驗研究； （ 6 ）提出了一種只要一個訓練樣本就能解決人臉識別問題的新方法，該方法結合了特徵矩陣、 fisher最優鑒別分析和廣義最優鑒別分析方法的優點。
  3. So the ability of resolving the uncertain problems represents the intelligence of system, and the reasoning model based on uncertainty has become a key research project in ai and expert system ( es ). uncertainty knowledge representation can be classified into two categories : probabilistic and non - probabilistic

    不確定知識表達的方法可分為兩大類：一類是基於概率的方法，包括信度網（ beliefnetwork ） 、動態因果圖（ dynamiccausalitydiagrams ） 、馬爾可夫網（ markovnetwork ）以及在專家系統prospector中使用的方法等。
  4. A temporal reasoning method based on probabilistic temporal network in situation assessment

    態勢估計中一種基於概率時間網路的時間推理方法
  5. This course explores the history and debates over codifying the laws of probability, how probability theory applies to specific cognitive processes, how it relates to the human understanding of causality, and how new computational approaches to causal modeling provide a framework for understanding human probabilistic reasoning

    本課程探索關于機率法則形成的歷史和爭論，機率理論如何運用於具體的認知過程，它與人類對因果的理解有著怎樣的關系，以及新的關于因果模型計算方法怎樣為理解人類的機率推理過程提供架構。
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