用C++实现的B

最新的Levenberg Marquardt 算法。用于非线性最小二乘问题的参数估计和优化！-Latest Levenberg Marquardt algorithm. Nonlinear least squares problem for parameter estimation and optimization!

财经类数值算法(finacial numerical recipes),压缩包内有pdf文件和c++源码-Financial category numerical algorithm (Financial numerical recipes), compressed within a pdf file and c-source

用opencv的方法实现的人脸识别，通过用opencv自带的分类器实现对人脸的识别，本人已试验过，可以实现对图片中正脸和侧脸的多角度识别，而且还可以改变一下实现对视频中或者是摄像头前的人脸识别，代码仅供参考

计算负载使用牛顿拉夫逊法 method.case ieee14 节点系统的流量。

c语言数值计算算法，包括各种排序算法、差值、迭代等-c language numerical algorithms, including sorting algorithm, the margin, such as iterative

使用决策树(Decision tree)分类算法、朴素贝叶斯（Naïve Bayes）算法或者K-近邻（kNN）算法（三者任选其一）对给定的训练数据集构造分类器，并在测试数据集上进行分类预测  

本书对Linux 早期操作系统内核(v0.95)全部代码文件进行了详细全面的注释和说明，旨在使读者能够在尽量短的时间内对Linux 的工作机理获得全面而深刻的理解，- This book (v0.95) the complete code document has carried on the detailed comprehensive annotation and the explanation to the Linux early time operating system essence, is for the purpose of enabling the reader to obtain comprehensive and the profound understanding in the as far as possible short time to the Linux work mechanism,

这是一个在能量控制下的，作用PF和UKF对比的，机器人控制语言，主要是C#编写。PF使用重采样算法，能够看出在PF系统下比UKF更好的预测结果，但是却需要更多计算时间。

k-means 算法的工作过程说明如下：首先从n个数据对象任意选择 k 个对象作为初始聚类中心；而对于所剩下其它对象，则根据它们与这些聚类中心的相似度（距离），分别将它们分配给与其最相似的（聚类中心所代表的）聚类；然后再计算每个所获新聚类的聚类中心（该聚类中所有对象的均值）；不断重复这一过程直到标准测度函数开始收敛为止。-k-means algorithm process as follows: First of all, the object data from the n choose k object as initial cluster centers and the remaining for the other object, then according to their cluster center with those of the similarity (distance) respectively assigned to them with the most similar (represented by cluster center) clustering obtained and then calculated for each new cluster center clustering (all objects in the cluster mean) repeated this process until the standard measure of function until the beginning of convergence.

在生产和实验中，通常需要依据一组实验数据，确定函数来近似该组数据，可以采用最小二乘法求解上述问题。-In the production and experiment, usually based on a set of experimental data to determine the function to approximate the set of data, least square method can be used to solve the aforementioned problems.