m__sequence

隐马尔科夫的MATLAB代码以及原理，希望对大家有帮助，哈哈哈哈哈(The MATLAB code, and Hidden Markov theory, we want to help,哈哈哈哈哈)

This is a case where a array is introduced

说明：  天线的3D方向图形成,又助于对天线整体的认识(3Dpatten )

一种小波增强图像的方法，过程详细，结果对比分明，有较好的参考价值。(Enhance the image of a wavelet method, the process in detail, comparing the results clearly, there is a good reference value.)

Facial Feature Addition and Alteration

汽车驱动力分析、行驶阻力分析和功率平衡图的绘制，根据汽车的12345档进行编程分析(Car driving force analysis, driving resistance analysis and graph drawing power balance, 12345 file programming analyzed according to the car)

单元平均二维恒虚警检测，常用于目标检测。(The average unit two-dimensional constant false alarm detection , commonly used in target detection .)

THESE ARE SOME IMPORTANT BASE PAPERS OF POWER ELECTRONICS

an lr-p implemention with matlab

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.)