Probabilistic abstraction hierarchies

Advances in Neural Information Processing Systems

Volumn , Issue , 2002, Pages

  Segal, Eran ^a   Koller, Daphne ^a   Ormoneit, Dirk ^a  

^a Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ABSTRACTING; GENE EXPRESSION; GLOBAL OPTIMIZATION; MAXIMUM LIKELIHOOD; TAXONOMIES;

ABSTRACTION HIERARCHY; CLUSTERING DATA; GLOBAL OPTIMIZATION ALGORITHM; HIERARCHICAL AGGLOMERATIVE CLUSTERING; JOINT LIKELIHOODS; PROBABILISTIC ABSTRACTION; PROBABILISTIC FRAMEWORK; PROBABILISTIC MODELING;

CLUSTERING ALGORITHMS;

EID: 84898938411     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. P. Cheeseman and J. Stutz. Bayesian Classification (AutoClass): Theory and Results. AAAI Press, 1995.
2. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, B 39:1-39, 1977.
3. M. Eisen, P. Spellman, P. Brown, and D. Botstein. Cluster analysis and display of genome-wide expression patterns. PNAS, 95:14863-68, 1998.
4. N. Friedman. The Bayesian structural EM algorithm. In Proc. UAI, 1998.
5. N. Friedman, M. Ninio, I. Peer, and T. Pupko. A structural EM algorithm for phylogentic inference. In Proc. RECOMB, 2001.
6. A.P. Gasch et al. Genomic expression program in the response of yeast cells to environmental changes. Mol. Bio. Cell, 11:4241-4257, 2000.
7. T. Hofmann. The cluster-abstraction model: Unsupervised learning of topic hierarchies from text data. In Proc. IJCAI, 1999.
8. T. Hofmann. The cluster-abstraction model: Unsupervised learning of topic hierarchies from text data. In Proc. International Joint Conference on Artificial Intelligence, 1999.
9. T. R. Hughes et al. Functional discovery via a compendium of expression profiles. Cell, 102(1):109-26, 2000.
10. F.K. Hwang, D.S.Richards, and P. Winter. The Steiner Tree Problem. Annals of Discrete Mathematics, Vol. 53, North-Holland, 1992.
11. A. Krogh, M. Brown, S. Mian, K. Sjolander, and D. Haussler. Hidden markov models in computational biology: Applications to protein modeling. Mol. Biology, 235:1501-1531, 1994.
12. A. McCallum, R. Rosenfeld, T. Mitchell, and A. Ng. Improving text classification by shrinkage in a hierarchy of classes. In Proc. ICML, 1998.
13. M. Meila and M.I. Jordan. Learning with mixtures of trees. Machine Learning, 1:1-48, 2000.
14. E. Segal and D. Koller. Probabilistic hierarchical clustering for biological data. In RECOMB, 2002.