calcul_couple

matlab code for kfcm

test Parsing Literals Source Code for Andriod.

说明：  快速单词拼写检错器，能实现一般的文本操作，另外还能检查单词拼写的对错，字典可以自定义。(rapid spelling error device, able to operate normally text, and checking the wrong spelling, dictionary can define.)

Matlab Image Processing Part 4

Detection of life threatening Arrhythmias are important for successful defibrillation therapy. This paper provides an idea about arrhythmia detection based on surface ECG analysis.

SVM分类程序，可以进行多类分割，该程序的优点是可以直接运行处结果，对SVM初学者十分有利。(Svm classfication program which could run fluently without boring small bugs.)

用matlab实现zigzag扫描，H.264帧内编码中不可缺少的环节(Zigzag scan using matlab implementation, H.264 intra-coded indispensable part in the)

blind image watermarking...........image hidding in image using SVD

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.)

to check for smaller eigen value