probabilistic algorithms 中文意思是什麼

probabilistic algorithms 解釋
概率演算法
  1. Topics covered include : randomized computation ; data structures ( hash tables, skip lists ) ; graph algorithms ( minimum spanning trees, shortest paths, minimum cuts ) ; geometric algorithms ( convex hulls, linear programming in fixed or arbitrary dimension ) ; approximate counting ; parallel algorithms ; online algorithms ; derandomization techniques ; and tools for probabilistic analysis of algorithms

    主題包括?隨機計算、資料結構（雜湊表、省略串列） 、圖論演演算法（最小擴張樹，最短路徑，最少切割） 、幾何演演算法（凸殼、在固定或任意維度的線性規劃） 、近似計數、平行演演算法、線上演演算法、消去隨機技術，以及演演算法的機率分析工具。
  2. It overcomes the limitation in the assumption in other semi - supervised learning algorithms that probabilistic distribution of data is known, and has the strong ability of learning new patterns and correcting errors because of stability and plasticity of the adaptive resonance theory

    在該系統中取消了一般半監督學習演算法中假定已知數據概率分佈的條件限制，利用自適應諧振理論的穩定性和可塑性，使其具有非常強的學習新模式和糾正錯誤能力。
  3. ( 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。
  4. The main factors of probabilistic neural network including the hidden neuron size, hidden central vector and the smoothing parameter, to influence the pnn classification, are analyzed ; the xor problem is implemented by using pnn. a new supervised learning algorithm for the pnn is developed : the learning vector quantization is employed to group training samples and the genetic algorithms ( ga ’ s ) is used for training the network ’ s smoothing parameters and hidden central vector for determining hidden neurons. simulations results show that, the advantage of our method in the classification accuracy is over other unsupervised learning algorithms for pnn

    本文主要分析了pnn隱層神經元個數，隱中心矢量，平滑參數等要素對網路分類效果的影響，並用pnn實現了異或邏輯問題;提出了一種新的pnn有監督學習演算法:用學習矢量量化對各類訓練樣本進行聚類，對平滑參數和距離各類模式中心最近的聚類點構造區域，並採用遺傳演算法在構造的區域內訓練網路，實驗表明:該演算法在分類效果上優于其它pnn學習演算法
  5. In this dissertation, several technology problems of pulse trains deintrleaving algorithms are dealt with, they are presorting techniques based on coherent processor, probabilistic neural network deinterleavers, adaptive data association methods for pulse trains analysis and deinterleaving, signal processor designing issues. the research is focused on real time processing. the coherent processor is a crucial technique for real time presorting

    本論文研究高密度復雜信號下的脈沖列去交錯技術的若干問題，包括基於關聯比較器的信號預分選技術研究；概率神經網路脈沖去交錯器的研究與設計；卡爾曼濾波和概率數據關聯方法用於脈沖列分析和去交錯；雷達截獲系統信號處理器設計等等。
  6. We can also see that these algorithms have better performance than the majority algorittim and compete algorithm, which are classic probabilistic algorithm in system level fault diagnosis. a distributed hierarchical diagnosis algorithm is discussed for virtual private networks

    對于診斷正確率，貪婪演算法遠遠好於majority演算法，並好於compete演算法；對于時間復雜度，與majority演算法相當，均為o （ n ~ 2 ） ，要好於compete演算法。
  7. At one time the thesis look back the part parallel interference cancellation detection, and update the algorithm of the multiuser with lms algorithm. at last, the thesis presentes the blind multiuser detection with adaptive algorithm the blind multiuser detection base on kalman algorithm and probabilistic algorithms for blind adaptive multiuser detection

    同時對部分并行干擾多用戶檢測器進行了回顧，並用lms演算法實現了多用戶檢測器的演算法更新。最後對盲多用戶檢測的自適應演算法進行了介紹，構造基於kalman濾波的盲多用戶檢測器，並對隨機梯度演算法進行了誤碼性能的分析。
  8. Based on the theory of probabilistic analysis of power systems, this paper studies the computer realization methods for commitment risk and response risk in generation systems. by using these methods, this paper focuses on the research of unit commitment, allocation and distribution algorithms of spinning reserve, considering the reliability requirement of generation systems. corresponding heuristic algorithm is given in this paper

    本文根據發電系統可靠性的概率分析理論，研究了發電系統中投運風險度和響應風險度的計算機實現方法，在此基礎上，重點討論了結合可靠性分析的機組組合方案、旋轉備用容量的確定和分配方案，並給出了相應的啟發式演算法。
  9. 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。
  10. In this paper, we describe the study background, meaning and methods of passive acoustic detective network, summarize the basic theories and methods of target tracking and data association, analyze some tipical data association algorithms include the nearest neighbor algorithm ( nn ), probabilistic data association filtering ( pdaf ), joint probabilistic data association filtering ( jpdaf ), multiple hypothesis tracking ( mht ), and multidimensional s - d assignment algorithm. 2. in detective network, sometimes a surveillance region have only single sensor

    從整體上描述了無源聲音探測網路的研究背景、意義、基本框架和研究方法，概述了目標跟蹤與數據關聯的基本理論與方法，重點分析了幾種典型的數據關聯方法，包括最近鄰方法、概率數據關聯濾波器（ pdaf ） 、聯合概率數據關聯濾波器（ jpdaf ） 、多假設跟蹤（ mht ）以及多維s - d分配演算法。
分享友人
分享到 Line

分享到臉書