Support vector regression has been proposed in a number of image processing tasks including blind image deconvolution, image denoising and single frame super-resolution. As for other machine learning methods, the training is slow. In this paper,

合稀疏贝叶斯学习(SBL)和可压缩传感理论(CS)，给出一种在噪声测量条件下重建可压缩图像的方法。该方法将cS理论中图像重建过程看作一个线性回归问题，而待重建的图像是该回归模型巾的未知权值参数；利用sBL方法对权值赋予确定的先验条件概率分布用以限制模型的复杂度，并引入超参数- Hop sparse Bayesian learning ( SBL ) and compressible sensing theory ( CS ) , give a compressible image recon

Closed Form Linear Regression Vs Gradient Descent in Machine Learning

进行逐步线性回归，用平面波展开法计算二维声子晶体带隙,是学习PCA特征提取的很好的学习资料。- Stepwise linear regression, Computation Method D phononic bandgap plane wave, Is a good learning materials to learn PCA feature extraction.