Combining supervised and unsupervised models via unconstrained probabilistic embedding

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2011, Pages 1396-1401

  Ma, Xudong ^a,c   Luo, Ping ^b   Zhuang, Fuzhen ^a,c   He, Qing ^a   Shi, Zhongzhi ^a   Shen, Zhiyong ^b  

^a INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^b HEWLETT PACKARD LABORATORIES   (United States)

^c UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ACCURACY; EMBEDDING METHOD; EMBEDDING PROBLEMS; ENSEMBLE LEARNING; EUCLIDEAN SPACES; GENERATIVE PROCESS; QUASI-NEWTON METHODS; REAL APPLICATIONS;

NEWTON-RAPHSON METHOD;

ARTIFICIAL INTELLIGENCE;

EID: 84881082801     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5591/978-1-57735-516-8/IJCAI11-236     Document Type: Conference Paper

References (11)

1. C.C.Chang and C.J. Lin. LIBSVM: a library for support vector machines. 2001.
2. X.Z. Fern and C.E. Brodley. Solving cluster ensemble problems by bipartite graph partitioning. In Proceedings of the twenty-first international conference on Machine learning, page 36. ACM, 2004.
3. J. Gao, F. Liang, W. Fan, Y. Sun, and J. Han. Graph-based consensus maximization among multiple supervised and unsupervised models. Advances in Neural Information Processing Systems (NIPS), 22:585-593, 2009.
4. A. Genkin, D.D. Lewis, and D. Madigan. Bbr: Bayesian logistic regression software. Center for Discrete Mathematics and Theoretical Computer Science, Rutgers, New Jersey, USA.[online] URL: http://www.stat.rutgers.edu/~madigan/BBR, 2005.
5. T. Iwata, T. Yamada, and N. Ueda. Probabilistic latent semantic visualization: topic model for visualizing documents. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 363-371. ACM, 2008.
6. G. Karypis. Cluto-family of data clustering software tools, 2006.
7. H.W. Kuhn. The Hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2):83-97, 1955.
8. M. Ley. DBLP bibliography. Available at http://www.informatik.uni-trier. de/ley/db/, 2001.
9. D.C. Liu and J. Nocedal. On the limited memory BFGS method for large scale optimization. Mathematical programming, 45(1):503-528, 1989. (Pubitemid 20660315)
10. A.K. McCallum, K. Nigam, J. Rennie, and K. Seymore. Automating the construction of internet portals with machine learning. Information Retrieval, 3(2):127-163, 2000.
11. A. Strehl and J. Ghosh. Cluster ensembles - a knowledge reuse framework for combining multiple partitions. The Journal of Machine Learning Research, 3:583-617, 2003.