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robust regression中文是什么意思

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  • The parametric motion models of human face are introduced firstly and then the face tracking system based on robust regression algorithm is developed
    介绍了人脸运动的参数模型,实现了一个基于鲁棒性回归算法的人脸跟踪系统。
  • In chapter 2 a robust learning algorithm of modular neural networks based on the theory of robust regression is presented . empirical study demonstrates that the robust learning algorithm of mnn has better precision and generalization than both modular neural networks and single neural network with the same robust algorithm , when trained under the contained dada
    第二章,本文提出了一种模块化神经网络的鲁棒学习算法,实验研究表明模块化神经网络的鲁棒学习算法对于污染样本的学习能获得较好的鲁棒性能和较高的学习精度,特别是在模型较复杂时,该算法的效果尤为明显。
  • Based on the review of the evolutions of stock indices and the innovations of index products , this article discussed the different methods of index replication , and then sum med up those researches on different methods , arithmetic models and their implications , including quadric programming , lineal programming , robust regression , monte carlo simulation and genetic algorithm , etc . aiming to give a technical reference for index derivatives design , index arbitrage , and indexing investment
    摘要在回顾证券价格指数演变及指数衍生品创新的基础上,探讨了指数复制的不同方法,进而从文献综述的角度对证券价格指数复制中涉及到的方法与算法模型进行整理,总结了二次规划、线性规划、鲁棒回归、蒙特卡洛模拟以及遗传算法等不同方法与模型的具体应用,为指数衍生品产品设计、指数套利以及实施指数化投资策略提供技术参考。
  • Chapter 4 introduces the use of weighted least square generalized svm for optimal control systems . weighted least square svm for robust regression estimation is generalized and then used to solve n - step optimal control problem based on the svm . the weights are determined by fuzzy method
    4 .加权ls一svm可以对回归估计问题实现鲁棒估计,把它同基于ls一svm的n步最优控制想法相结合,将其扩展到基于加权最小二乘广义svm ,并应用于n步最优控制问题,确定加权因子采用了模糊运算的办法。
  • 百科解释
In robust statistics, robust regression is a form of regression analysis designed to circumvent some limitations of traditional parametric and non-parametric methods. Regression analysis seeks to find the relationship between one or more independent variables and a dependent variable.
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Last modified time:Tue, 12 Aug 2025 00:29:56 GMT

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