Using Tarjan's red rule for fast dependency tree construction

Advances in Neural Information Processing Systems

Volumn , Issue , 2003, Pages

  Pelleg, Dan ^a   Moore, Andrew ^a  

^a Carnegie Mellon University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONFIDENCE INTERVAL; CORRELATION MATRIX; DEPENDENCY TREES; EFFICIENT LEARNING; MUTUAL INFORMATIONS; PARTIAL KNOWLEDGE; POLYNOMIAL-TIME; SPANNING TREE ALGORITHMS;

POLYNOMIAL APPROXIMATION;

EID: 10044251730     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Nir Friedman, Iftach Nachman, and Dana Peer. Learning bayesian network structure from massive datasets: The "sparse candidate" algorithm. In Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (UAI-99), pages 206-215, Stockholm, Sweden, 1999.
2. C. K. Chow and C. N. Liu. Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, 14A62-461, 1968.
3. Marina Meila. Learning with Mixtures of Trees. PhD thesis, Massachusetts Institute of Technology, 1999.
4. N. Friedman, M. Goldszmidt, and T. J. Lee. Bayesian Network Classification with Continuous Attributes: Getting the Best of Both Discretization and Parametric Fitting. In Jude Shavlik, editor, International Conference on Machine Learning, 1998.
5. Robert Endre Tarjan. Data structures and network algorithms, Volume 44 of CBMSNSF Reg. Conf. Ser Appl. Math. SIAM, 1983.
6. Oded Maron and Andrew W. Moore. Hoeffding races: Accelerating model selection search for classification and function approximation. In Jack D. Cowan, Gerald Tesauro, and Joshua Alspector, editors, Advances in Neural Information Processing Systems, Volume 6, pages 59-66, Denver, Colorado, 1994. Morgan Kaufmann.
7. Andrew W. Moore and Mary S. Lee. Efficient algorithms for minimizing cross validation error. In Proceedings of the 11th International Conference on Machine Learning (ICML-94), pages 190-198. Morgan Kaufmann, 1994.
8. Pedro Domingos and Geoff Hulten. Mining high-speed data streams. In Raghu Ramakrishnan, Sal Stolfo, Roberto Bayardo, and Ismail Parsa, editors, Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-00), pages 71-80, N. Y, August 20-23 2000. ACM Press.
9. Pedro Domingos and Geoff Hulten. A general method for scaling up machine learning algorithms and its application to clustering. In Carla Brodley and Andrea Danyluk, editors, Proceeding of the 17th International Conference on Machine Learning, San Francisco, CA, 2001. Morgan Kaufmann.
10. Pedro Domingos and Geoff Hulten. Learning from infinite data in finite time. In Proceedings of the 14th Neural Information Processing Systems (NIPS-2001), Vancouver, British Columbia, Canada, 2001.
11. Marina Meila. An accelerated Chow and Liu algorithm: fitting tree distributions to high dimensional sparse data. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML-99), Bled, Slovenia, 1999.
12. Fazlollah Reza. An Introduction to Information Theory, pages 282-283. Dover Publications, New York, 1994.
13. Dan Pelleg and Andrew Moore. Using Tarjan's red rule for fast dependency tree construction. Technical Report CMU-CS-02-116, Carnegie-Mellon University, 2002.
14. C.L. Blake and C.J. Merz. UCI repository of machine learning databases, 1998. http://www.ics.uci.edu/~mlearn/MLRepository.html.
15. S. Hettich and S. D. Bay. The UCI KDD archive, 1999. http://kdd.ics.uci. edu.