em algorithm 中文意思是什麼

em algorithm 解釋
em演演算法
  • em : n 1 字母 M m 2 【印刷】全身〈12點的字母 m〉。3 歐美文字排版的字行長度單位。pron 〈pl 〉 〈口語〉...
  • algorithm : n. 【數學】演算法；規則系統；演段。
  1. Studies on correct convergence of the em algorithm for gaussian mixtures

    演算法正確收斂性的探討
  2. Research on unsupervised chinese segmentation based on em algorithm

    一種改進的漢語分詞演算法
  3. Study of tracking maneuvering targets in clutter based on the em algorithm

    性能優化的跟蹤門演算法
  4. We first introduce the em algorithm and its convergence properties

    首先，我們引入了em演算法，討論了它的一般收斂性質。
  5. We also give a variation of the em algorithm for gaussian mixtures

    進一步給出了高斯混合體em演算法的變形及其收斂性質。
  6. Correction of control point slope based on em algorithm and shading of single sar image

    演算法和單幅雷達圖像陰影的控制點坡度校正
  7. 18 dempster a, laird n, rubin d. maximum likelihood from incomplete data via the em algorithm

    進一步，數據降維的結果可通過與兩個空間相關度量的最大似然解得到。
  8. In this paper, we present a theoretical analysis on the correct convergence of the em algorithm for gaussian mixtures

    本文對高斯混合體em演算法的正確收斂性問題進行了理論研究。
  9. However, there has been still an important but unsolved problem : whether the em algorithm can converge to the correct solution

    然而， em演算法能否收斂到正確解一直是一個困難的問題。
  10. In this article, we apply em algorithm to estimating the values of the parameters of nhpp models. the estimating accuracy may be increased by this way. so the result software reliability analysis may be improved by this way too

    最後本文應用文獻[ 33 ]中介紹的em演算法于nhpp類模型的參數估計，以提高估計的精度，從而提高軟體可靠性分析的精確程度。
  11. What flow is that, we use model simulation to analyze the em algorithm contraction ratio. through network simulating, we analyze the factors which can influence loss inference algorithm accuracy like measurement strategy or routing algorithm. we analyze the accuracy and contraction characteristic of multicast - based direct algorithm and em algorithm, and compare the error factor between them

    實驗中通過網路模擬模型，確定了em演算法的收斂速率;研究了不同測量策略和路由器擁塞避免演算法對丟包率推理演算法準確率的影響;分析了單點多播的de和em演算法準確性、收斂性等特徵，通過比較兩種演算法的統計誤差，得出em演算法略優于de演算法的結論。
  12. That is, the convergence speed of the em algorithm becomes much fast as the overlap measure of gaussians in the mixture tends to zero, which conforms to simulation results and practical applications

    當這一重疊度趨向于零時，演算法的收斂速度漸進地趨向于超線性，變成為一個快速演算法。這與實際應用是相符合的。
  13. Journal of machine learning research, 2004, 5 : 913 - 939. 16 dempster a p, laird n m, rubin d b. maximum likelihood from incomplete data via the em algorithm

    這個一般性的法則揭示了多示例學習與監督學習之間的聯系，為多示例學習演算法的設計提供了一種通用的途徑。
  14. We then prove that the em algorithm becomes a compact map in certain neighborhood of a consistent solution when a measure of the average overlap of gaussians in the mixtures is small enough and the sample size is large enough

    我們證明了高斯混合體em演算法在混合密度的重疊度很小時在其樣本真解相一致的解的一個鄰域內是一個壓縮映射。
  15. A wavelet domain hidden markov tree ( hmt ) model is constructed to model statistical dependence and nongaussian statistics of wavelet coefficient. the estimate of hmt model parameters can be obtained by em algorithm

    該方法通過小波域的隱markov樹（ hmt ）模型來描述小波系數的統計相關性和非高斯性，利用em演算法獲得hmt模型參數的估計。
  16. It uses gassian mixture model to represent particles and adopts em algorithm to refit particles after correction step at each time

    該演算法使用混合高斯模型表示粒子，在每個時刻的修正步驟之後，採用em演算法對粒子進行重新擬合。
  17. Such integra ting feature vector is used for building k dim e nsion gaussian m odel, whose param e ters are estim ated by an expectation - m a xi m i zation ( em ) algorithm, and then the resulting block - cluster m e mberships provide a segm entation of th e im age. after segm ented, a m e thod of param e ter - trimm e d average for describing re gion is proposed, of which the param e ter is decided by area and position of region dire ctly. the sim ilarity m easure between two im ages is defined by integrating properties of all regions in the im age

    文中先將圖像分成4 4小塊，各塊的顏色、紋理、位置特徵構成8維的特徵空間；在該空間中對得到的8維特徵矢量建立一個k維高斯模型，應用期望最大em演算法估計模型參數，產生的塊特徵-聚類隸屬度函數實現對圖像的分割；為減小分割演算法不確定性對檢索效果的不良影響，對得到的區域採用參數均衡平均特徵表示，其中參數的確定直接與區域的面積、位置有關。
  18. It follows from the general convergence theory that the em algorithm generally converge to a local maximum solution of the likelihood function and cannot be guaranteed to converge to a correct solution, i. e., a consistent solution of the samples

    Em演算法的一般收斂理論認為，演算法只能收斂到似然函數的一個局部極大解，無法保證能夠收斂到與樣本的真實參數相一致的解上。
  19. But in practical applications and experiments, we often find that em algorithm for gaussian mixtures can converge correctly when the overlap measure of component densities in the mixtures becomes small

    但在實際應用與實驗中，我們經常發現當重疊度較小時，概率混合體em演算法往往收斂到正確解。
  20. Actually, it has been found that the convergence rate of the em algorithm for gaussian mixtures is related to the overlap measure of gaussians in the mixture in certain situation

    近年來，對于高斯混合體em演算法的收斂性研究有了新的進展。人們發現演算法的收斂速度與高斯混合體各分量之間的重疊度有關。
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