The free semi-supervised learning or transductive inference
software SemiL (written by Te Ming Huang and developed by
Te Ming Huang and Vojislav Kecman, with a support of Dr.
Chan-Kyoo Park. Discussion with and help of Dr. Dengyong
Zhou is highly appreciated) implements, extends and improves
two approaches presented in papers,
Zhou, D., Bousquet, O., Lal, T. N., Weston, J., Schölkopf,
Learning with Local and Global Consistency, NIPS 16,
pp. 321-328, 2004
Zhu, X.-J., Ghahramani, Z., Lafferty, J.:
Learning Using Gaussian Fields and Harmonic Functions, ICML
and it was initiated during the short stay of the first
author at the Max Planck Institute (Department B. Schölkopf).
SemiL is efficient software for solving large scale semi-supervised
learning or transductive inference problems using graph based
approaches when faced with unlabeled data. It implements
various semi-supervised learning approaches as listed below:
- Hard label approach with the maximization of smoothness,
- Soft label approach with the maximization of smoothness,
- for all three types of models (i.e., Basic model, Norm
Constrained Model and Bound Constrained Model) by
using either Standard or Normalized Laplacian.
Download the zipped software (SemiL.zip 1.7MB) by clicking
After unzipping, it will self-extract itself into
the folder SemiL. Read the User Manual first, and
play with SemiL under Windows (MS-DOS, Command
Prompt) or under a Linux platform.
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