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Statistical and Machine Learning Approaches for Network Analysis
book

Statistical and Machine Learning Approaches for Network Analysis

by Matthias Dehmer, Subhash C. Basak
August 2012
Intermediate to advanced content levelIntermediate to advanced
344 pages
10h 30m
English
Wiley
Content preview from Statistical and Machine Learning Approaches for Network Analysis

References

1. L. Getoor, B. Taskar, Introduction, in Introduction to Statistical Relational Learning, (L. Getoor, B. Taskar, eds.), MIT Press, Cambridge, Massachusetts, pp. 1–8, 2007.

2. Z. Harchaoui, F. Bach, Image classification with segmentation graph kernels, in Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Minneapolis, Minnesota, 2007.

3. R. Grishman, S. Zhao, Extracting relations with integrated information using kernel methods, in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics (ACL), Ann Arbor, Michigan, pp. 419–426, 2005.

4. R.C. Bunescu, R.J. Mooney, A shortest path dependency kernel for relation extraction, in Proceedings of the Human Language Technology Conference on Empirical Methods in Natural Language Processing (HLT/EMNLP), Vancouver, Canada, pp. 724–731, 2005.

5. F. Reichartz, H. Korte, G. Paass, Dependency tree kernels for relation extraction from natural language text, in Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Bled, Slovenia, pp. 270–285, 2009.

6. S. Ghosh, P. Mitra, Combining content and structure similarity for xml document classification using composite svm kernels, in 19th International Conference on Pattern Recognition (ICPR), Tampa, Florida, pp. 1–4, 2008.

7. A. Moschitti, R. Basili, A tree kernel approach to question and answer classification in question answering systems, in In Proceedings ...

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Publisher Resources

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