梯度下降法求解回归问题

我的这个是S3C44B0键盘结构化代，测试OK-This is my keyboard structured S3C44B0 generation, test OK

自己学的文件加密小程序，希望对你们有用，欢迎下载-Their own to learn a small file encryption program that you want to be useful are welcome to download

说明：  ufmod 1.25.2a source code for windows

Php使用技巧集 文件格式 chm 文件大小：61K()

vibe算法，需配置opencv，用于检测运动目标(The classic vibe algorithm needs to be configured with OpenCV to detect moving targets.)

说明：  无线水位检测，通过JC24联系两块电路板，进行无线传输的(Wireless water level detection)

对采集的图像或者振动信号进行软硬阈值处理，进而达到降噪的效果(The soft and hard thresholds of the collected images or vibration signals are processed to achieve the effect of noise reduction.)

说明：  用N来表示训练用例（样本）的个数，M表示特征数目。 输入特征数目m，用于确定决策树上一个节点的决策结果；其中m应远小于M。 从N个训练用例（样本）中以有放回抽样的方式，取样N次，形成一个训练集（即bootstrap取样），并用未抽到的用例（样本）作预测，评估其误差。 对于每一个节点，随机选择m个特征，决策树上每个节点的决定都是基于这些特征确定的。根据这m个特征，计算其最佳的分裂方式。 每棵树都会完整成长而不会剪枝，这有可能在建完一棵正常树状分类器后会被采用）。(N is used to represent the number of training cases (samples), and M is used to represent the number of features. The number of input features m is used to determine the decision result of a node in the decision tree, where m should be far less than m. From N training cases (samples), n times are sampled in the way of put back sampling to form a training set (i.e. bootstrap sampling), and the unselected cases (samples) are used to predict and evaluate the error. For each node, m features are randomly selected, and the decision of each node in the decision tree is determined based on these features. According to these m characteristics, the best splitting mode is calculated. Each tree will grow completely without pruning, which may be adopted after building a normal tree classifier).)

Java Applet小程序图片显示实例，Applet小程序中装载一幅照片并显示出来，有需要此方面资料的Java爱好者可下载参考本源码。

利用MATLAB中的GUI实现傅里叶变换的时移特性(Time-Shift Characteristics of Fourier Transform Implemented by GUI)