MGLM Toolbox for Matlab
-
Updated
Mar 23, 2017 - MATLAB
MGLM Toolbox for Matlab
Knowledge elicitation when the user can give feedback to different features of the model with the goal to improve the prediction on the test data in a "smal n, large p" setting.
Generalized Orthogonal Least-Squares in CUDA
Sparse Bayesian ARX models with flexible noise distributions
MATLAB library of gradient descent algorithms for sparse modeling: Version 1.0.3
Black-box spike and slab variational inference, example with linear models
Sparse Identification of Truncation Errors (SITE) for Data-Driven Discovery of Modified Differential Equations
(now superseded by MLJLinearModels)
Implemented and Compared Algorithms Solving Sparse Penalized Regression
Variable Selection and Task Grouping for Multi-Task Learning (VSTG-MTL)
The Method of Entropic Regression, sparse system identification method based on causality inference of complex networks.
STELA algorithm for sparsity regularized linear regression (LASSO)
Sequential adaptive elastic net (SAEN) approach, complex-valued LARS solver for weighted Lasso/elastic-net problems, and sparsity (or model) order detection with an application to single-snapshot source localization.
Factored QTL analysis applied to GTEx and GWAS of 114 complex traits
a collection of modern sparse (regularized) linear regression algorithms.
This repository stores my personal projects related to data science studies.
Assignment: Linear and Sparse Regression Consider the attached dataset about advertising and sales. The attributes denote the investments on advertising in TV, radio etc and the target variable is the total sales. The aim is to predict the sales from the investments on advertising. 1) Randomly divide the dataset into training (75%) and testing (…
This work presents the application of machine learning models in order to obtain a sparse governing equation of complex fluid dynamics problems.
SparseStep: Approximating the Counting Norm for Sparse Regularization
Hybrid Approach to Sparse Group Fused Lasso
Add a description, image, and links to the sparse-regression topic page so that developers can more easily learn about it.
To associate your repository with the sparse-regression topic, visit your repo's landing page and select "manage topics."