sirkedekhabo

利用matlab来分析主成分，得出权重等一些参数(Use matlab to analyze the principal components, obtained some parameters such as weight)

通信系统matlab仿真可能用到的程序，比如高斯噪声，功率算法，并联与直联系统的转换等(Telecommunication system may be used in matlab simulation , for instance gaussian noise, power algorithm, parallel and straight league system conversion etc)

这是GMSD算法的matlab程序，描述了在图像去噪方法的作用(This is GMSD algorithm matlab program, describes the role of image de-noising method)

说明：  深度神经网络在函数近似中表现优越，然而需要从头开始训练。另一方面，贝叶斯方法，像高斯过程（GPs），可以利用利用先验知识在测试阶段进行快速推理。然而，高斯过程的计算量很大，也很难设计出合适的先验。本篇论文中我们提出了一种神经模型，条件神经过程（CNPs），可以结合这两者的优点。CNPs受灵活的随机过程的启发，比如GPs，但是结构是神经网络，并且通过梯度下降训练。CNPs通过很少的数据训练后就可以进行准确的预测，然后扩展到复杂函数和大数据集。我们证明了这个方法在一些典型的机器学习任务上面的的表现和功能，比如回归，分类和图像补全(Deep neural networks perform well in function approximation, but they need to be trained from scratch. On the other hand, Bayesian methods, such as Gauss Process (GPs), can make use of prior knowledge to conduct rapid reasoning in the testing stage. However, the calculation of Gauss process is very heavy, and it is difficult to design a suitable priori. In this paper, we propose a neural model, conditional neural processes (CNPs), which can combine the advantages of both. CNPs are inspired by flexible stochastic processes, such as GPs, but are structured as neural networks and trained by gradient descent. CNPs can predict accurately with very little data training, and then extend to complex functions and large data sets. We demonstrate the performance and functions of this method on some typical machine learning tasks, such as regression, classification and image completion.)

SVM工具箱集锦，希望对大家有帮助，SVM现在发展很快啊！(SVM Toolbox Collection, I hope all of you help, SVM has developed very rapidly now ah!)

说明：  支节调配器法的软件实现（微波技术与天线）(Support section of the deployment of browser software law)

Soure for fault classification using machine intelligence technique

amba specifications document

用matlab编写实现图像的分块功能、小波矩特征提取、直方图均衡化，可以供大家参考使用(Image segmentation is implemented by Matlab code)

一种新型自适应信号时频处理方法，特别适用于非线性非平稳信号的分析处理。(A new adaptive signal time-frequency processing method is proposed, which is especially suitable for the analysis and processing of non-linear and non-stationary signals.)