A domain adaptation toolbox

Version 1.0.0.0 (4.68 MB) by Ke Yan
Wrappers and implementations of several domain adaptation / semi-supervised learning algorithms
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Updated 4 Jun 2016
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Wrappers and implementations of several domain adaptation / transfer learning / semi-supervised learning algorithms, including:
* Transfer Component Analysis (TCA)
* Maximum Independence Domain Adaptation (MIDA)
* Subspace Alignment (SA)
* Information-Theoretical Learning (ITL)
* Geodesic flow kernel (GFK)
* Stationary Subspace Analysis (SSA)
* Laplacian SVM (LapSVM)
* Laplacian ridge regression (LapRR)
* Transducive SVM (TSVM)
* (Kernel) PCA (KPCA)
Please find details at: http://yanke23.com/articles/research/2016/04/17/A-domain-adaptation-matlab-toolbox.html
or https://github.com/viggin/domain-adaptation-toolbox

Cite As

Ke Yan (2024). A domain adaptation toolbox (https://github.com/viggin/domain-adaptation-toolbox), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2010b
Compatible with any release
Platform Compatibility
Windows macOS Linux

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infometric_0.1

lapsvmp_v02

lapsvmp_v02/classifiers

lapsvmp_v02/graph

lapsvmp_v02/gui

lapsvmp_v02/libsvm

plslda

svml-master

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Version Published Release Notes
1.0.0.0

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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.