Commercial_Airplane

求第一个城市到其它城市的最短路径.用矩阵（为顶点个数）存放各边权的邻接矩阵，行向量、、、分别用来存放标号信息、标号顶点顺序、标号顶点索引、最短通路的值(The first city to find the shortest path to other cities. With a matrix (for the number of vertices) records of the right side of the adjacency matrix, row vector, were used to store label information and label the vertex order, label the vertex indexes, the shortest pathway value)

指纹识别系统，调试成功后的源码，带有图片，matlab 7.0 直接运行(Fingerprint identification system, source code debugging success, with pictures directly run matlab 7.0)

基于BP算法的 求解最优L1范数的程序和文章(BP algorithm based on L1 norm for solving optimal procedures and articles)

Newton插值公式实例，改变插值节点个数，观察不同的插值函数的逼近效果.(Instance of the Newton interpolation formula, change the number of interpolation nodes, and observe the different interpolation function approximation.)

南极三维坐标转换算法,三维平面坐标系到极方位等角坐标系(3d coordinate system transform of antarctic)

WIRESLESS SENSOR NETWORKS ALGORTHIM6

主要用于sar成像算法学习，用来生成目标回波，并用RD算法进行成像处理。(Sar imaging algorithm is mainly used for learning, used to generate the target echo imaging with RD algorithm.)

数字图像处理与机器视觉-visual C++与matlab实现,第8章配套源程序(Digital image processing and machine vision of-visual C++ and MATLAB, the 8th chapter matching source)

Fuzz c-mean Clusterin technique

说明：  相关向量机（Relevance Vector Machine，简称RVM）是Micnacl E.Tipping于2000年提出的一种与SVM（Support Vector Machine）类似的稀疏概率模型，是一种新的监督学习方法。 它的训练是在贝叶斯框架下进行的，在先验参数的结构下基于主动相关决策理论（automatic relevance determination，简称ARD）来移除不相关的点，从而获得稀疏化的模型。在样本数据的迭代学习过程中，大部分参数的后验分布趋于零，与预测值无关，那些非零参数对应的点被称作相关向量（Relevance Vectors），体现了数据中最核心的特征。同支持向量机相比，相关向量机最大的优点就是极大地减少了核函数的计算量，并且也克服了所选核函数必须满足Mercer条件的缺点。(Relevance Vector Machine (RVM) is a sparse probability model similar to SVM (Support Vector Machine) proposed by Micnacl E. Tipping in 2000. It is a new supervised learning method. Its training is carried out under the Bayesian framework. Under the structure of prior parameters, it is based on Automatic Relevance Determination (ARD) to remove the irrelevant points, so as to obtain the sparse model. In the iterative learning process of sample data, the posterior distribution of most parameters tends to zero, which is independent of the predicted value. The points corresponding to non-zero parameters are called Relevance Vectors, which represent the most core features of the data. Compared with support vector machine, the biggest advantage of correlation vector machine is that it greatly reduces the computation amount of kernel function, and also overcomes the shortcoming that the selected kernel function must meet Mercer's condition.)