Context-based statistical relational learning: Thesis

作者: Yonghong Tian

DOI:

关键词:

摘要: The relational structure is an important source of information, which often ignored by the traditional statistical learning methods. Thus this thesis focuses on how to explicitly exploit such information in tasks so as build more effective and robust models. main methodology used derived from context-based modeling analysis. Several models algorithms are investigated different viewpoints context, thereby demonstrating general applicability learning.

参考文章(12)
Lise Getoor, Benjamin Taskar, Nir Friedman, Daphne Koller, Learning Probabilistic Models of Relational Structure international conference on machine learning. pp. 170- 177 ,(2001)
Chalee Asavathiratham, The influence model : a tractable representation for the dynamics of networked Markov chains Massachusetts Institute of Technology. ,(2001)
David Heckerman, David Maxwell Chickering, Christopher Meek, Robert Rounthwaite, Carl Kadie, Dependency networks for inference, collaborative filtering, and data visualization Journal of Machine Learning Research. ,vol. 1, pp. 49- 75 ,(2001) , 10.1162/153244301753344614
Tian Yong-hong, Huang Tie-jun, Gao Wen, None, Exploiting multi-context Analysis in Semantic Image Classification Journal of Zhejiang University Science. ,vol. 6, pp. 1268- 1283 ,(2005) , 10.1007/BF02841665
Yonghong Tian, Tiejun Huang, Wen Gao, Latent linkage semantic kernels for collective classification of link data intelligent information systems. ,vol. 26, pp. 269- 301 ,(2006) , 10.1007/S10844-006-2208-9
Yonghong Tian, Qiang Yang, Tiejun Huang, C.X. Ling, Wen Gao, Learning Contextual Dependency Network Models for Link-Based Classification IEEE Transactions on Knowledge and Data Engineering. ,vol. 18, pp. 1482- 1496 ,(2006) , 10.1109/TKDE.2006.178
Benjamin Taskar, Nir Friedman, Daphne Koller, Lisa Getoor, Learning probabilistic models of link structure Journal of Machine Learning Research. ,vol. 3, pp. 679- 707 ,(2003)