glm-ie: The Generalised Linear Models Inference and Estimation Toolbox

Nickisch, H

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Journal Article

glm-ie: The Generalised Linear Models Inference and Estimation Toolbox

MPS-Authors

/persons/resource/persons84109

Nickisch, H
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Nickisch, H. (2012). glm-ie: The Generalised Linear Models Inference and Estimation Toolbox. Journal of Machine Learning Research, 13, 1699-1703.

Cite as: https://hdl.handle.net/11858/00-001M-0000-000E-FDAE-0

Abstract

{The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some The code is fully compatible to both Matlab 7.x and GNU Octave 3.3.x. Abstract Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework.}