SVM半监督图像分类

车间作业调度问题遗传算法Matlab源码,用于解决车间的调度问题(Job Shop Scheduling Problem Matlab genetic algorithm source code for the solution to the scheduling problem workshop)

目标作蛇形机动matlab仿真，包括卡尔曼和最小二乘滤波，可出x，y方向误差，(Matlab simulation target for maneuver, including the Kalman and least square filtering, the x, y direction of error,)

信息论中变长编码的香农编码程序，大家可以互相学习(Variable-length coding in information theory Shannon coding procedures, we can learn from each other)

Spacecraft Engineering Toolbox

压缩感知的几种算法算法,帮助更深入了解压缩感知算法(Compressed Sensing)

本文为Stephen J.Chapman《MATLAB 编程（第二版）》英文影印版的中文译本，这本 书对初学者很好的入门教材。(This article Stephen J.Chapman " MATLAB Programming (second edition)," The Chinese translation of the English version of a photocopy, this book is a good primer for beginners.)

说明：  经典图书Courant, Hilbert - Methods of Mathematical Physics 第二卷，50年不倒的经典图书，值得一看，是学习数学物理方法的经典名著(Classic books Courant, Hilbert- Methods of Mathematical Physics II, 50 years will not be brought down classic books, worth a visit is to study methods of mathematical physics classics)

利用三电平中心削波自相关法实现清、浊音判定，并提取一段话音的基频参数，绘制基频曲线，并将该参数曲线合并到前面提取的短时过零率和短时能量参数曲线图中(Using a three-level center clipping from the relevant law to achieve clear, voiced determination, and extract some of the fundamental parameters of the voice, drawing the fundamental frequency curve and merge the parametric curve to the front of the extraction rate and the short-term zero short-term energy parameters graph)

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.)

authorware小例子 1、打台球 2、古诗欣赏 3、投篮 4、移动图形 5、制作一个钟表(authorware small example 1, billiards 2, 3 poetry appreciation, shooting four, five mobile graphics to produce a watch)