搜索资源列表
OWLQN
- source codes for \"Orthant-Wise Limited-memory Quasi-Newton Optimizer for L1-regularized Objectives\"
libsvm-2.89
- 是一種線性方成的分類器。SVM透過統計的方式將雜亂的資料以NN的方式分成兩類，以便處理。LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, and L1-loss linear SVM. -Main features of LIBLINEA
AprioriMain
- 此算法实现了基本的Apriori算法,效率很低. 过程是:先通过对数据集进行扫描,得到候选1-项集C1,根据用户输入的最小支持度筛选出频繁1-项集L1,将筛选中 不满足条件的结果放入一个先验项集,然后对L1进行组合,并根据Apriori算法的先验原理,用每个组合的结果和先 验项集中的所有元素进行比较,如果组合结果的子集中包含先验集中的任何一个元组就将其排除,将没有被排除 的组合结果放入C2.如此循环反复,直到Cn或Ln为空. 2008.11.1-2008.11.3
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
Sparse-Reconstruction_2.0
- 基于分散近似的稀疏重建算法,用于l1-l2规划，图像去噪，图像压缩等。-sparse reconstruction by separable approximation
00471413
- donoho等人写的关于BP算法的一片文献，比较有参考价值-Basis Pursuit is a technique for decomposing a signal into an "optimal" superposition of dictionary elements. The optimization criterion is the l1 norm of coeficients.
GPS-cycle-slip-processing
- GPS L1,L2双拼周跳探测与修复，包含几种常用的实时探测算法，附有GUI界面-A MATLAB software package for GPS cycle-slip processing is presented in this paper. It realizes cycle-slip detection and repair in the measurement domain for GPS L1 and L2 signals. The software implement
SSR_DOA
- 稀疏表示doa估计，l1范数凸优化的算法，性能很好-Low complexity method for DOA estimation using array covariance matrix sparse representation
icml07scalable
- owlqn, 微软L1最优化算法论文，源码也已上传-owlqn, L1 optimization
l1magic
- 求解l1范式的工具包，包含许多例子，应用比较广泛-Solving the l1 paradigm toolkit, contains many examples, application more widely
l1_magic
- l1_magic用于稀疏表示求解L1稀疏系数，特点是本程序是用于求解稀疏系数比l1_ls快-this procedure is used to solve the following problem。 min ||x||1 s.t. y=Ax
SolveLasso
- 解决lasso问题的MATLAB代码，可以很方便的求解L1范数问题-to solve the lasso problem
Huber
- 机器学习ADMM Huber损失最小化1范式约束算法Sparse Huber With L1 Constrain And Alternating Direction Method of Multipliers Method-Sparse Huber With L1 Constrain And Alternating Direction Method of Multipliers Method