Geoscience Reference
In-Depth Information
APPENDIX
B
Semi-Supervised Learning
Software
This appendix contains an annotated list of software implementations of semi-supervised learning
algorithms available on the Web. The codes are organized by the type of semi-supervised models
used. We have tried our best to provide up-to-date author affiliations.
CLUSTER-BASED
Title:
Low Density Separation
Authors:
Olivier Chapelle (Yahoo! Research), Alexander Zien (Friedrich Miescher Laboratory of
the Max Planck Society)
Description:
Matlab/C implementation of the low density separation algorithm. This algorithm
tries to place the decision boundary in regions of low density, similar to Transductive SVMs.
Related papers:
[
36
]
Title:
Semi-Supervised Clustering
Author:
Sugato Basu (Google)
Description:
Code that performs metric pairwise constrained k-means clustering. Must-link and
cannot-link constraints specify requirements for how examples should be placed in clusters.
Related papers:
[
15
,
16
]
GRAPH-BASED
Title:
Manifold Regularization
Author:
Vikas Sindhwani (IBM T.J. Watson Research Center)
Description:
Matlab code that implements manifold regularization and contains several other
functions useful for different types of graph-based learning.
