Search - lasso algorithm

Search - lasso algorithm - List

[Other resource] GenLASSO-1.1.tar DL : 0: 统计学习软件包，基于lasso算法的参数估计-statistical learning packages, lasso based on the parameter estimation algorithm
Update : 2008-10-13 Size : 439.52kb Publisher : Dune Lee
[Algorithm] GenLASSO-1.1.tar DL : 0: 统计学习软件包，基于lasso算法的参数估计-statistical learning packages, lasso based on the parameter estimation algorithm
Update : 2025-02-19 Size : 440kb Publisher : Dune Lee
[Mathimatics-Numerical algorithms] lasso DL : 0: lasso算法是stanford的tibshirani提出的一种变量子集选择算法。-lasso algorithm is proposed by stanford
Update : 2025-02-19 Size : 202kb Publisher : 项龙江
[Algorithm] Lasso DL : 2: lasso regression. It is very good to use for statics processing.
Update : 2025-02-19 Size : 351kb Publisher : Tandy
[Special Effects] ssa DL : 0: 多种信号过完备字典学习算法的工具包，包含文献Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms中所有的算法。-Multiple signals over-complete dictionary learning algorithm toolkit, including literature Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms for all algorithms.
Update : 2025-02-19 Size : 13kb Publisher :
[Special Effects] LiveWireFinal DL : 0: 交互式图像分割的一种算法——LiveWire在VC++平台下基于opencv的实现。类似于Photoshop中吸铁石套索的功能。-Interactive image segmentation algorithm- LiveWire platform in VC++ opencv-based implementation. Similar to the magnet lasso feature in Photoshop.
Update : 2025-02-19 Size : 13.84mb Publisher : 杨榕
[matlab] lars DL : 1: 解L1正则化回归问题（lasso）的Lars算法 -a classic algorithm for lasso called LARs——Least Angle Regression
Update : 2025-02-19 Size : 3kb Publisher : 柚子
[matlab] SALSA_v2.0 DL : 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
Update : 2025-02-19 Size : 236kb Publisher : yangzhenzhen
[Graph Drawing] PolyFill DL : 0: 多边形扫描转换填充算法(即,抠图或多边形套索) -Polygon scan conversion filling algorithm (ie, matting or polygonal lasso)
Update : 2025-02-19 Size : 36kb Publisher : 卓峰
[Other] code DL : 0: 处理lasso regression。使用stochastic Markov property。-Algorithm for solving the Lasso problem: 0.5* (y- X*beta) *(y- X*beta)+ lambda* ||beta||_1 where ||beta||_1 is the L_1 norm i.e., ||beta||_1 = sum(abs( beta )) We use the method proposed by Fu et. al based on single co-ordinate descent. For more details see GP s notes or the following paper: Penalized Regressions: The Bridge Versus the Lasso Wenjiang J. FU, Journal of Computational and Graphical Statistics, Volume 7, Number 3, Pages 397?416, 1998
Update : 2025-02-19 Size : 4kb Publisher : 莫琳
[Other] multi-task_lasso DL : 0: MMT is a Matlab toolbox implementing the multi-task Lasso models, including: (i) the Lasso (ii) the standard multi-task Lasso, i.e. the group Lasso (iii) the structured input-output multi-task Lasso, a.k.a. the two-graph guided multi-task Lasso proposed in [1]. The last case (iii) subsumes the special cases: tree-guided and the feature-graph guided multi-task Lasso. The core optimization algorithm for solving this model is developed in C to enhance greater computational efficiency. In particular, current scalability of the coefficient matrix that has been tested for MMT is 104*104! The structured input-output multi-task Lasso model is well-suited for addressing the expression quantitative trait loci (eQTL) mapping problems which are of the intrinsic high-dimensional nature. Details can be found in [1].-MMT is a Matlab toolbox implementing the multi-task Lasso models, including: (i) the Lasso (ii) the standard multi-task Lasso, i.e. the group Lasso (iii) the structured input-output multi-task Lasso, a.k.a. the two-graph guided multi-task Lasso proposed in [1]. The last case (iii) subsumes the special cases: tree-guided and the feature-graph guided multi-task Lasso. The core optimization algorithm for solving this model is developed in C to enhance greater computational efficiency. In particular, current scalability of the coefficient matrix that has been tested for MMT is 104*104! The structured input-output multi-task Lasso model is well-suited for addressing the expression quantitative trait loci (eQTL) mapping problems which are of the intrinsic high-dimensional nature. Details can be found in [1].
Update : 2025-02-19 Size : 467kb Publisher : 莫琳
[matlab] lasso DL : 0: lasso算法，分为很多子类，可以单独运行-lasso algorithm
Update : 2025-02-19 Size : 54kb Publisher : 李松
[Multimedia Develop] Motion_aware-decoding-of-CS-video DL : 0: 一篇关于压缩感知的运动感知译码算法，将YUV视频进行压缩感知采样，然后传输至译码端，并采用LASSO算法对其进行重构的文章。-A motion perception on compressed sensing decoding algorithm, the YUV video compression sensing sampling, and then transmitted to the decoder side, using LASSO algorithm to reconstruct the article.
Update : 2025-02-19 Size : 3.59mb Publisher : like
[Special Effects] lasso DL : 0: 关于LSAAO算法的MATLAB程序代码以及对应的参考文献资料。-MATLAB code about LSAAO algorithm as well as the corresponding reference data.
Update : 2025-02-19 Size : 17.92mb Publisher : zhangxingbo
[Algorithm] lasso DL : 0: lasso算法的例子，用于最小二乘优化,包含了多个子文件-an example of lasso algorithm for least square update，which includes a lot subfiles
Update : 2025-02-19 Size : 36kb Publisher : jinghoko
[AI-NN-PR] Unconstrain-lasso DL : 0: 在传统LASSO 算法基础上，未加约束的lasso源码，有在这方面应用的可以拿去使用-Based on the traditional LASSO algorithm, without the constraints of lasso source code, application in this area can use it
Update : 2025-02-19 Size : 4kb Publisher : 赵傲雪
[DataMining] lasso-enet DL : 1: 针对数据挖掘中的lasso算法，可用于选择变量-Data driven algorithm for lasso based regression modelling which can be used for variable selection
Update : 2025-02-19 Size : 32kb Publisher : zchww
[Special Effects] lasso DL : 0: 使用admm算法解决lasso问题，matlab代码-Use admm lasso algorithm to solve the problem, matlab codes
Update : 2025-02-19 Size : 1kb Publisher : john
[Other] LASSO DL : 0: 基于LASSO算法的编程利用了r语言.对线性模型进行了系数估计和变量选择(Programming based on LASSO algorithm, using the R language)
Update : 2025-02-19 Size : 7kb Publisher : hujing0731
[matlab] lasso DL : 0: 2012matlab含有此工具箱。优化算法。(2012matlab contains this toolbox. Optimization algorithm.)
Update : 2025-02-19 Size : 53kb Publisher : 尛宝天天见714

« 12 »