熵極小化 的英文怎麼說
中文拼音 [shāngjíxiǎohuà]
熵極小化
英文
entropy minimization-
Kullback ' s cross - entropy function is tried to smooth the minimal ncp - function and a non - interior continuation method is constructed for lcps
本章嘗試用叉熵函數來光滑化極小化ncp函數。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，簡單描述了熵正則化方法與罰函數法的研究現狀；第二章，針對有限極大極小問題，通過研究熵正則化方法與指數（乘子）罰函數方法之間的關系，揭示熵正則方法的數學本質；第三章將極大熵方法推廣到一般不等式約束優化問題上，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，給出一種構造罰函數的統一框架，並通過具體的罰和障礙函數例子加以說明。Firstly, the duality principle is used to change the linear program problem into the minimax problem, an interval extension of the adjustable entropy function is set up and its order of convergence is discussed
首先利用對偶理論將線性規劃問題轉化為極大極小問題，建立並討論了調節熵函數的區間擴張及其收斂階。In this thesis, we extend the entropy regularization method in two ways : from the min - max problem to general inequality constrained optimization problems and from the entropy function to more general functions
本文從兩個方面發展了這種熵正則化方法，即將其從極大極小問題推廣到一般不等式約束優化問題上和用一般函數代替熵函數作正則項，建立新的正則化方法。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上引進shannon信息熵作正則項，給出一致逼近極大值函數的光滑函數。An information entropy - based uncertainty measure is presented first based on generalized rough set model in this paper, which is suitable for evaluating rules retrieved from noisy data. second, this paper puts forward generalized minimal - and - maximal - rules - learning methods and generalized maximal - minimal - rules - conversion model because we can encounter noisy problems in most real - life problems. third, this paper puts forward a new discretization method for the continuous attributes, which is based on the clustering and rough sets theory
本文在對粗集及其相關理論的研究基礎上，首先給出了一種基於推廣粗集模型和信息熵的規則不確定性量度，該不確定性量度適于評價從有噪音數據中提取的規則；鑒于實際應用中經常能遇到噪音的問題，本文提出廣義極小極大規則學習方法，同時還提出了廣義極大極小規則轉換模型gmm ；最後，本文基於聚類方法、結合粗集理論提出了一種新的連續屬性離散化方法。分享友人