lianxudianji

SVSLMS算法的参数设定值不同时的比较SVSLMS算法的参数设定值不同时的比较(SVSLMS algorithm parameter settings are not at the same time comparison SVSLMS algorithm parameter settings comparison of different time)

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)

HSQARKH 完整源码，这只是一个初步代码,用来实现ARK,做隐藏方面研究的. (HSQARKH complete source code, this is just a preliminary code that is used to achieve ARK, so hidden aspects of the study.)

Robust cost functions that based on M-Estimators and used as cost functions in order to robustify the learning algorithms for feed-forward neural networks

matpower程序，包括4、6、9、30、36、118节点等case。(matpower program, including the case of 4,6,9,30,36,118 node.)

adaboost程序，演示程序，直接可以运行，修改部分程序即可运用到自己的应用中(adaboost, presentation program can run directly modify parts of the program can be applied to their own applications)

adaptive binary arithmetic coding (CABAC) is a method of entropy coding first introduced in H.264/AVC and now used in the newest standard High Efficiency Video Coding (HEVC).

kantz方法计算 Lyapunov 指数的 Matlab 程序的文件(kantz calculated Lyapunov index of Matlab procedures for the)

很全的时频分析工具，很全的时频分析工具。。(Is the whole time-frequency analysis tool, is the whole time-frequency analysis tools. .)

(1) 读取n副连续有重叠部分的图像，在n副图像中检测SIFT特征，并用SIFT 特征描述子对其进行描述。 (2) 匹配相邻图像的特征点，并根据特征点向量消除误匹配。 (3) 使用RANSAC方法，确定变换参数。 (4) 图像融合 ((1) Read n successive overlapping sub-part of the image, the image of the n sub-SIFT features detected and characterized using SIFT descriptors be described. (2) adjacent to the image feature point matching, and according to the feature vector to eliminate false matching points. (3) using RANSAC approach to determine the transformation parameters. (4) image fusion)