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maximum entropy method in Chinese

Pronunciation:
How to pronounce "maximum entropy method""maximum entropy method" in a sentence

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  • 最大熵法

Examples

  • Maximum entropy method for nonlinear l1 problem
    1問題的極大熵方法
  • Maximum entropy method for linear programming with bounded variables
    變量有界線性規(guī)劃的極大熵方法
  • Maximum entropy method and quadratic programming sub - problem ' s explicit solution
    極大熵方法與二次規(guī)劃子問題的顯式解
  • In this thesis the basic principle of maximum entropy method ( mem ) and its applications in image restoration are thoroughly discussed
    最大熵方法是一種非線性的圖像恢復(fù)方法，過去的研究主要圍繞天文圖像的處理而進(jìn)行。
  • A context - dependent polyphone disambiguation technology was proposed , combined with the decision tree and maximum entropy methods , the accuracy can be improved to 99 . 82 %
    采取結(jié)合上下文信息及語法信息的多音字消歧技術(shù)，利用決策樹和最大熵等模式識(shí)別方法，使注音正確率達(dá)到99 . 82 % 。
  • According to the maximum entropy method , a program applied to compute reliability of cwr dynamic stability is worked out and put into practice to analyze reliability of cwr dynamic stability , getting the safety allowable temperature under design reliability index
    根據(jù)反應(yīng)極值最大熵?cái)M合法編制了無縫線路動(dòng)力穩(wěn)定性可靠度計(jì)算程序，分析了高速鐵路無縫線路動(dòng)力穩(wěn)定性的可靠度，得到滿足目標(biāo)可靠指標(biāo)的允許溫升。
  • Maximum entropy method is an effective smoothing one for the finite min - max problem , which , by adding shannon ' s informational entropy as a regularizing term to the lagrangian function of min - max problem , yields a smooth function that uniformly approaches the non - smooth max - valued function
    極大熵方法是解有限極大極小問題的一種有效光滑化法，它通過在極大極小問題的拉格朗日函數(shù)上引進(jìn)shannon信息熵作正則項(xiàng)，給出一致逼近極大值函數(shù)的光滑函數(shù)。
  • The second chapter reveals the mathematical essence of entropy regularization method for the finite min - max problem , through exploring the relationship between entropy regularization method and exponential penalty function method . the third chapter extends maximum entropy method to a general inequality constrained optimization problem and establishes the lagrangian regularization approach . the fourth chapter presents a unified framework for constructing penalty functions by virtue of the lagrangian regularization approach , and illustrates it by some specific penalty and barrier function examples
    第一章為緒論，簡(jiǎn)單描述了熵正則化方法與罰函數(shù)法的研究現(xiàn)狀；第二章，針對(duì)有限極大極小問題，通過研究熵正則化方法與指數(shù)(乘子)罰函數(shù)方法之間的關(guān)系，揭示熵正則方法的數(shù)學(xué)本質(zhì)；第三章將極大熵方法推廣到一般不等式約束優(yōu)化問題上，建立了拉格朗日正則化方法；第四章利用第三章建立的拉格朗日正則化方法，給出一種構(gòu)造罰函數(shù)的統(tǒng)一框架，并通過具體的罰和障礙函數(shù)例子加以說明。
  • Further , some knowledge of information theory are introduced to describe the regularity of process system calls such as the sequential dependency relationship between neighboring system calls . the use of information theory provides theoretic proof for creating more effective detecting models . in the end , applying for the maximum entropy method in information theory , a maximum entropy model of intrusion detection is presented
    同時(shí)，為了提高入侵檢測(cè)的準(zhǔn)確性，又在原有的馬爾可夫鏈模型的基礎(chǔ)上，引進(jìn)信息理論中的信息熵，條件熵等概念來更好地刻畫進(jìn)程系統(tǒng)調(diào)用序列的時(shí)序依賴關(guān)系，從而為建立更好的檢測(cè)模型提供信息理論依據(jù)。
  • More examples：  1  2

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