Geoscience Reference
In-Depth Information
100 BIBLIOGRAPHY
[64] David Elworthy. Does Baum-Welch re-estimation help taggers? In
Proceedings of the 4th
Conference on Applied Natural Language Processing
, 1994.
DOI: 10.3115/974358.974371
[65] Jason D.R. Farquhar, David R. Hardoon, Hongying Meng, John Shawe-Taylor, and Sandor
Szedmak. Two view learning: SVM-2K, theory and practice. In
Advances inNeural Information
Processing Systems (NIPS)
. 2006.
[66] Akinori Fujino, Naonori Ueda, and Kazumi Saito. A hybrid generative/discriminative ap-
proach to semi-supervised classifier design. In
AAAI-05, The Twentieth National Conference
on Artificial Intelligence
, 2005.
[67] Akinori Fujino, Naonori Ueda, and Kazumi Saito. Semisupervised learning for a
hybrid generative/discriminative classifier based on the maximum entropy principle.
IEEE Transaction on Pattern Analysis and Machine Intelligence
, 30(3):424-437, 2008.
[68] Glenn Fung and Olvi Mangasarian. Semi-supervised support vector machines for unlabeled
data classification. Technical Report 99-05, Data Mining Institute, University of Wisconsin
Madison, October 1999.
[69] Jochen Garcke and Michael Griebel. Semi-supervised learning with sparse grids. In
Proc. of
the 22nd ICMLWorkshop on Learning with Partially ClassifiedTraining Data
, Bonn, Germany,
August 2005.
[70] Gad Getz, Noam Shental, and Eytan Domany. Semi-supervised learning - a statistical physics
approach. In
Proc. of the 22nd ICML Workshop on Learning with Partially Classified Training
Data
, Bonn, Germany, August 2005.
[71] J.J. Godfrey, E.C. Holliman, and J. McDaniel. Switchboard: telephone speech corpus for
research and development.
Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992
IEEE International Conference on
, 1:517-520 vol.1, March 1992.
[72] Andrew B. Goldberg, Ming Li, and Xiaojin Zhu. Online manifold regularization: A
new learning setting and empirical study. In
The European Conference on Machine Learn-
ing and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)
, 2008.
[73] Andrew B. Goldberg and Xiaojin Zhu. Seeing stars when there aren't many stars: Graph-
based semi-supervised learning for sentiment categorization. In
HLT-NAACL 2006Workshop
on Textgraphs: Graph-based Algorithms for Natural Language Processing
, New York, NY, 2006.
[74] Andrew B. Goldberg, Xiaojin Zhu, Aarti Singh, Zhiting Xu, and Robert Nowak. Multi-
manifold semi-supervised learning. In
Twelfth International Conference on Artificial Intelligence
and Statistics (AISTATS)
, 2009.
