搜索资源列表
LogisticR
- Logistic Loss with the L1-norm Regularization
LeastR
- LeastR Least Squares Loss with the L1-norm Regularization-Least Squares Loss with the L1-norm Regularization
nnLeastR
- Least Squares Loss with the L1-norm Regularization subject to non-negative constraint
nnLogisticR
- Logistic Loss with the L1-norm Regularization subject to non-negative constraint
L1precision
- 使用MAP估计基于L1正则化的DAG，是该领域最顶尖的Kevin Murphy开发的-MAP estimation of DAG based on L1 regularization
L1General
- L1General是一个求解各种L1-正则化问题的Matalb程序。详细说明可参考：http://www.di.ens.fr/~mschmidt/Software/L1General.html -L1General is a set of Matlab routines implementing several of the available strategies for solving L1-regularization problems.
SALSA_v2.0
- 应用交替方向乘子法来求解L1正则化问题、BP问题、LASSO问题的一种算法，-Application alternating direction multiplier method to solve L1 regularization problem, BP issue LASSO problem an algorithm
CRFtools.zip
- CRFsuite: a fast implementation of Conditional Random Fields (CRFs) CRFSuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data. The first priority of this software is to train and use CRF models as fast as possi
Duality-Splitting-L1-TV
- 用对偶分裂算法处理乘性噪声如盐椒噪声（L1+TV），参数由电脑自动选取，matlab程序-A Duality-Based Splitting Method for L1-TV Image Restoration with Automatic Regularization Parameter Choice
reconstruction_algorithms
- 本代码主要给出了激光粒度仪颗粒散射光强分布以及4种粒度反演算法，以及4种算法之间的比较。四种反演算法为：TSVD、Chaine、Tikhonov和l1正则化。-The code gives the Zetasizer particle scattering intensity distribution, and four kinds of particle inversion algorithm, as well as a comparison between the four algorith
super-resolution-Regularization-
- 本程序包括了三个程序，L1范数正则化，L2范数正则化，Tikhonov正则化超分辨率重建。经反复测试，没有BUG。-The program includes three procedures, L1 norm regularization, L2 norm regularization, Tikhonov regularization super-resolution reconstruction. After repeated testing, no BUG.
predict-and-match-interal-multiple
- 地震信号处理，虚同相轴方法预测层间多次波，将数据分成上下两部分，利用相关和褶积的原理预测出层间多次波。预测信号和原始信号在相位和振幅上存在差异，用L1范数匹配法进行匹配，其中，提供了两种方法解病态方程，分别为高斯-赛德尔方法和正则化方法。-Seismic signal processing, predicte internal multiples by construct virtual events .The data is divided into two parts, using the
L1
- L1 Normalization for quadratic regularization
l1_regularized-LSP
- 压缩感知信号重构算法，基于L1正则化的重构算法，可以学习学习。-Compressed sensing signal reconstruction based on L1 regularization reconstruction algorithms ，solve l1-regularized least squares problems
sunsal
- 稀疏中的基于加权L1正则化的SUNSAL算法-The sparse based on weighted L1 regularization algorithm SUNSAL
CSR_Denoising
- 该算法首先通过字典学习得到含噪图像的冗余字典，然后对相似的图像块进行聚类构成块群，并通过迭代收缩和L1正则化约束，对同类的图像块在字典上进行稀疏表示，以达到降噪的目的。实验结果表明，在常规的图像处理上，本文提出的算法能较好的保留图像的结构信息，与K-SVD和BM3D等现有的流行算法相比，具有更高的峰值信噪比（PSNR）-It firstly get the redundant dictionary of a noised image by dictionary learning.Then,the
L1 Total Variation
- 利用L1范数TV正则化对影像进行超分辨率重建(Super resolution reconstruction of images using L1 norm TV regularization)
SolveDALM
- l1正则化算法中的一种，用于计算矩阵方程(One of the L1 regularization algorithms used to compute matrix equations)
BregmanCookbook_v32
- 这是用于l1正则化功能的bregman算法，主要用于图像去噪，去模糊，去卷积等(This is a Bregman algorithm for L1 regularization, which is mainly used for image denoising, blur, deconvolution, etc.)
TVL1denoise
- 正则化去除噪声,效果撮合，凑合。。。。。。(Tikhonov regularization)