正则项 meaning in Chinese
regular terms
Examples
- When adding an entropy function as regularizing term to the lagrangian function , we obtain a smooth approximate function for m ( x ) , which turns out to be the exponential penalty function
当将熵函数作为正则项加到拉格朗日函数上，我们得到了逐点逼近于m ( x )的光滑函数。经证明，该函数即为指数罚函数。 - 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
本文从两个方面发展了这种熵正则化方法，即将其从极大极小问题推广到一般不等式约束优化问题上和用一般函数代替熵函数作正则项，建立新的正则化方法。 - By contriving appropriate algorithms in the computations , the regularized parameter can automatically correct to the appropriate value due to its adaptive character , meanwhile , the adaptive effect is developed with a weight matrix included in the regularized item
运算中合理地设计算法,自适应的正则化参数可以自动修正到最优值,同时自适应加权的正则项使算法的性能更加完善。 - 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
极大熵方法是解有限极大极小问题的一种有效光滑化法，它通过在极大极小问题的拉格朗日函数上引进shannon信息熵作正则项，给出一致逼近极大值函数的光滑函数。 - This thesis introduces and analyses in detail the research and application of regularization in image restoration , the main contributions and creativities are listed as follows : firstly , on the basis of the regularization technique for dealing with ill - posed problems , starting from the mathematical theories about regularization , analyzing the degraded model and the ill - posed character of image restoration , this paper mainly discusses the regularized parameter and regularized item , and the solution approaches and fast algorithms are also summarized , therefore perfects the essential theories about regularization in image restoration
本文针对图像复原中正则化方法的研究及应用进行了广泛深入的探讨与分析,主要工作和创新点如下:第一,在正则化技术解决病态问题的基础上,从正则化方法数学理论入手,分析了图像的退化模型和图像复原的病态特征,重点讨论了正则化参数和正则项的选取,并总结了其求解算法及快速实现方法,完善了正则化方法图像复原的基本理论。