shiyanfive

unconstrain function matlab code, optimizition benchmark problem (test problem) in matlab

Trade FX with SVM and Matlab

This code computing unicriterion net flow scores in PROMETHEE II in matlab programming.

三类目标的散射点模型，分别建立模型，并且画图，三个文件代表三类对象(Three types of target scattering point model)

detail explanation of matlab

matlab算法，用bp网络进行训练，然后进行分类的一种算法，很成功(matlab algorithm, using bp network training, followed by a classification algorithm, very successful)

说明：  基本的直方图绘制方法： （1）直方图的绘制； （2）直方图均衡化(The basic method of drawing histogram: (1) Draw the histogram (2) Histogram equalization)

matlab的全英文书籍，用于更好的理解和编写matlab原编码，希望有帮助(matlab full English books, to better understand and prepare for the matlab source code, hope that helps)

matlab 数据导入导出及相关实例分析。做的不好，请指教(matlab data import and export, and analyze relevant examples. Do not, please advise)

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.)