| Semi-Supervised Learning
|
Often, it is easy and cheap to obtain large amounts of unlabelled data
(e.g. images, text documents), while it is hard or expensive to obtain
labelled data. Semi-supervised learning methods attempt to use the
unlabelled data to improve the performance on supervised learning
tasks, such as classification.
Some relevant publications:
- Zhu, X., Kandola, J., Lafferty, J. and Ghahramani, Z. (2006)
Graph Kernels by Spectral
Transforms.
In Chapelle, O., Schoelkopf, B. and Zien, A. (eds) Semi-Supervised
Learning. MIT Press.
- Zhu, X., Kandola, J., Ghahramani, Z. and Lafferty, J. (2005)
Nonparametric Transforms of Graph
Kernels for Semi-Supervised Learning.
In Advances in Neural
Information Processing Systems 17. (NIPS-2004)
- Zhu, X., Lafferty, J., and Ghahramani, Z. (2003)
Semi-Supervised Learning: From Gaussian
Fields to Gaussian Processes.
CMU tech report CMU-CS-03-175 , 2003.
[gzipped ps | pdf]
- Zhu, X.,
Lafferty, J. and Ghahramani, Z. (2003)
Combining
Active Learning and Semi-Supervised Learning Using
Gaussian Fields and Harmonic Functions.
In Proc. of the ICML 2003 workshop on The Continuum from
Labeled to Unlabeled Data in Machine Learning and Data
Mining. pp. 58-65
[gzipped ps | pdf]
-
Zhu, X., Ghahramani, Z. and Lafferty, J.(2003)
Semi-Supervised Learning Using Gaussian Fields
and Harmonic Functions
The Twentieth
International Conference on Machine Learning
(ICML-2003). pp 912-919[gzipped ps
| pdf]
-
Jin, R. and Ghahramani, Z. (2003)
Learning with Multiple
Labels.
In Advances in Neural Information Processing
Systems 15. Cambridge, MA: MIT Press.
-
Zhu, X., and Ghahramani, Z. (2002) Towards Semi-Supervised Classification with Markov Random Fields
CMU CALD tech report CMU-CALD-02-106, 2002. [pdf]
-
Zhu, X., and Ghahramani, Z. (2002) Learning from Labeled and Unlabeled Data with Label Propagation
CMU CALD tech report CMU-CALD-02-107, 2002. [gzipped ps | pdf | short version]