converters.tar

本书是关于模式识别与机器学习的一本经典教材，其中更对贝叶斯原理做了详细的描述，更能帮助读者了解贝叶斯方法在模式识别中的重要作用。(This book is a classic textbook on pattern recognition and machine learning abroad, in particular the principle of Bayesian detailed description.)

mathematic全美经典教程，中文版本，非常实用。(mathematic language tutorials, Chinese)

绝对好的！用matlab实现的车牌识别程序，包含字符提取和字符切割模块的程序和字符模板(Absolutely good! License plate recognition using matlab implementation procedures, including character extraction and character segmentation module of the program and character templates)

离散数据点三维数据的图相模拟m代码，通用版本(three-demension drawing in the matlab)

This function is for training a codebook for vector quantization. The data set is split to two clusters, first, and the mean of each cluster is found (centroids). The disttance of each vector from these centroids is found and each vector is associated with a cluster. The mean of vectors of each cluster replaces the centroid first. If the total distance is not improved substantially, The centroids are each split to two. This goes on untill the required number of clusters is reached and the improvement is not substantial.

使用matlab实现混合图像的显示功能，使其随着距离的变化而显示出不同的图像(Using matlab hybrid image display function)

详细描述继承的概念，让你更好的理解C++中的继承(Detailed description of the concept of inheritance, so you better understand the C++, inheritance)

Wireshark. The following reference answers are based on the trace files

1） 借助MATLAB画出误差性能曲面和误差性能曲面的等值曲线； 2） 写出最陡下降法， LMS算法的计算公式（ ）； 3） 用MATLAB产生方差为0.05,均值为0白噪音S(n)，并画出其中一次实现的波形图； 4） 根据2）中的公式，并利用3）中产生的S(n)，在1）中的误差性能曲面的等值曲线上叠加画出采用最陡下降法， LMS法时H(n)的在叠代过程中的轨迹曲线。 5）用MATLAB计算并画出LMS法时 随时间n的变化曲线（对 应S(n)的某一次的一次实现）和e(n)波形；某一次实现的结果并不能从统计的角度反映实验的结果的正确性，为得到具有统计特性的实验结果，可用足够多次的实验结果的平均值作为实验的结果。用MATLAB计算并画出LMS法时J(n)的100次实验结果的平均值随时间n的变化曲线。 6）用MATLAB计算并在1）中的误差性能曲面的等值曲线上叠加画出LMS法时100次实验中的H(n)的平均值的轨迹曲线； 7）对以上实验结果给出一些你认为有价值的讨论。 （在实验中n=1,,…..N,N的取值根据实验情况确定，一般选取足够大以使算法达到基本收敛，本题作业以电子文档PDF格式提供） (1) With MATLAB to draw the contours of the error performance surface and error performance surface 2) Write down the steepest descent method, the LMS algorithm formula () 3) using MATLAB variance 0.05, mean 0 white noise S (n), and draw one to achieve the waveform diagram 4) According to the formula 2), and using 3) S (n) produced in 1), the error performance of the contours of the surfaces superimposed on draw using the steepest descent method, the LMS method is H (n) in the iterative process in the trajectory curve. 5) curve to calculate and draw the LMS method using MATLAB with time n (time corresponding to the S (n) of a first implementation) and e (n) waveform reflect a certain time to achieve results not from a statistical point of experiment the correctness of the results obtained with the experimental results of the statistical properties of the available sufficient times the average of the experimental results as a result of the experiment. Calculate and draw the LMS m)

sample gausian mixture