Collective Classification for Text Classification - Text Mining: Classification, Clustering, and Applications - page 65

3.8 Conclusion
In this chapter, we gave a brief description of four popular collective classi-
fication algorithms. We explained the algorithms, showed how to apply them
to various applications using examples and highlighted various issues that
have been the subject of investigation in the past. Most of the inference algo-
rithms available for practical tasks relating to collective classification are ap-
proximate. We believe that a better understanding of when these algorithms
perform well will lead to more widespread application of these algorithms to
more real-world tasks and that this should be a subject of future research.
3.9 Acknowledgments
This material is based upon work supported in part by the National Science
Foundation under Grant No.0308030.
References
[1] S. M. Aji and R. J. McEliece. The generalized distributive law and free
energy minimization. In Proceedings of the 39th Allerton Conference on
Communication, Control and Computing , 2001.
[2] D. Anguelov, B. Taskar, V. Chatalbashev, D. Koller, D. Gupta, G. Heitz,
and A. Ng. Discriminative learning of markov random fields for seg-
mentationof3dscandata. In IEEE Computer Society Conference on
Computer Vision and Pattern Recognition , 2005.
[3] C. Berrou, A. Glavieux, and P. Thitimajshima. Near Shannon limit
error-correcting coding and decoding: Turbo codes. In Proceedings of
IEEE International Communications Conference , 1993.
[4] J. Besag. On the statistical analysis of dirty pictures. Journal of the
Royal Statistical Society , 1986.
[5] Y. Boykov, O. Veksler, and R. Zabih. Fast approximate energy mini-
mization via graph cuts. IEEE Transactions on Pattern Analysis and
Machine Intelligence , 2001.
[6] V. Carvalho and W. W. Cohen. On the collective classification of email
speech acts. In Special Interest Group on Information Retrieval , 2005.