搜索资源列表
Newton_Method
- 自己根据<最优化理论与基础>课本编的matlab程序 最优化问题求解: 牛顿法 自助输入目标函数及其梯度-Optimization: Newton Method Type in objective function and its gradient by yourself.
Gold_sector
- 自己根据<最优化理论与基础>课本编的matlab程序 黄金分割法 自助输入目标函数及其梯度-Optimization: Gold Section Method Type in objective function and its gradient.
comp_n5_1
- 面板单位根检验的matlab代码,基于最小绝对偏差估计的混合自助法的面板单位根检验方法,-panel unit root test matlab code, which is based on the LAD estimation and Hybrid bootstrap
AOMUSv0.72.tar
- 自助推理可满足性研究算法。用于求解极小不可满足子式的随即搜索算法。-Self-study reasoning algorithm can meet. For solving minimal unsatisfiable sub-formula then search algorithm.
mediawiki-1.16.1.tar
- 开放式交流平台,自助安装的网站服务器,维基media-Open communication platform, self-installed web server
self-service-and-obstacles
- 无人系统自助航路规划及自助避碰程序仿真算法发。-Unmanned systems and self-service buffet collision avoidance route planning program simulation algorithm hair.
examples-of-fuzzy-neural-network
- 学习模糊神经网络的例子,能帮助初学者自助学习。做一个参考-the examples of fuzzy neural network
bootstrap_mfile
- bootstrap自助法,实现小样本的扩充,并且的达到估计均值区间的作用。-Self bootstrap method to achieve the expansion of small samples, and the estimated mean interval to the role.
latpart
- Latin-partition主要是一种交叉验证方法,由professor Peter及其课题组开发,用于模型的稳定性和有效性验证评价,,可以作为自助多次重复划分校正集和验证集,避免重复随机数据集建模所产生的不可靠性。-Latin-partition is primarily a cross-validation method, professor Peter and developed by the Task Force for the stability and validity evalu
Bootstrap
- 用visual basic编写的自助法数值解法计算程序-Bootstrap values solution prepared using visual basic calculation program
bootstrap
- 自助法(Bootstrap)的matlab源码,在小样本统计学中很有用-matlab code for Bootstrap method
Washingalgorithm
- 利用模糊算法,实现洗衣机对有渍衣物的自助清洗-Fuzzy algorithm, there are stains on the clothes washing machine self-cleaning
重抽样与自助法
- 当数据抽样于非正态分布时,如未知或混合分布、样本量过小、存在离群点、基于理论分布设计合适的统计检验过于复杂且数学上难以处理等情况,这时基于随机化和重抽样的统计方法可派上用场。(When the sampling data in non normal distribution, such as the unknown or mixed distribution, the sample size is too small, there are outliers, based on the theor
机器学习之随机森林
- Bagging是并行式集成学习方法最著名的代表,Bagging通常对分类任务使用简单投票法,随机森林(RF)是Bagging的一个扩展变体,RF在以决策树为基学习器构建Bagging 集成的基础上,进一步在决策树的训练过程中引入了随机属性选择。在RF中,集成模型的每棵树构建时所需的样本都是由训练集经过有放回的随机抽样得来(即自助采样法bootstrap sample)。