Computing optimal hypotheses efficiently for boosting

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2281, Issue , 2002, Pages 471-481

  Morishita, Shinichi ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; DECISION TREES; FORESTRY; TREES (MATHEMATICS);

BOOLEAN TESTS; CLASSIFICATION TASKS; HIGH-ACCURACY; HIGHLY ACCURATE; MAJORITY VOTE; OPTIMIZATION ALGORITHMS; OVERFITTING; TRAINING DATA;

ADAPTIVE BOOSTING;

EID: 84867658655     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45884-0_35     Document Type: Article

References (16)

1. R. Agrawal, T. Imielinski, and A. N. Swami. Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D.C., May26-28, 1993, pages 207-216. ACM Press, 1993.
2. R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In VLDB'94, Proceedings of 20th International Conference on Very Large Data Bases, September 12-15, 1994, Santiago de Chile, Chile, pages 487-499. Morgan Kaufmann, 1994.
3. E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(2):105-139, 1999.
4. J. Bentley. Programming pearls. Communications of the ACM, 27(27):865-871, Sept. 1984.
5. S. Brin, R. Rastogi, and K. Shim. Mining optimized gain rules for numeric attributes. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discoveryand Data Mining, 15-18 August 1999, San Diego, CA USA, pages 135-144. ACM Press, 1999.
6. C. Domingo and O. Watanabe. A modification of adaboost: A preliminary report. Research Reports, Dept. of Math. and Comp. Sciences, Tokyo Institute of Technology, (C-133), July 1999.
7. Y. Freund. Boosting a weak learning algorithm by majority. Information and Computation, 121(2):256-285, 1995.
8. Y. Freund and R. E. Schapire. Experiments with a new boosting algorithm. In Machine Learning: Proceedings of the Thirteenth International Conference, pages 148-156, 1996.
9. Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119-139, Aug. 1997.
10. T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama. Data mining using twodimensional optimized accociation rules: Scheme, algorithms, and visualization. In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Quebec, Canada, June 4-6, 1996, pages 13-23. ACM Press, 1996.
11. S. Khanna, S. Muthukrishnan, and M. Paterson. On approximating rectangle tiling and packing. In Proceedings of the Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, pages 384-393, Jan. 1998.
12. S. Morishita. On classification and regression. In Proceedings of DiscoveryScienc e, First International Conference, DS'98 - Lecture Notes in Artificial Intelligence, volume 1532, pages 40-57, Dec. 1998.
13. S. Morishita and J. Sese. Traversing itemset lattices with statistical metric pruning. In Proc. of ACM SIGACT-SIGMOD-SIGART Symp. on Database Systems (PODS), pages 226-236, May 2000.
14. R. E. Schapire. The strength of weak learnability (extended abstract). In FOCS, pages 28-33, 1989.
15. H. Tamaki and T. Tokuyama. Algorithms for the maxium subarray problem based on matrix multiplication. In Proceedings of the Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, pages 446-452, Jan. 1998.
16. K. Yoda, T. Fukuda, Y. Morimoto, S. Morishita, and T. Tokuyama. Computing optimized rectilinear regions for association rules. In Proceedings of the Third International Conference on Knowledge Discoveryand Data Mining, pages 96-103, Aug. 1997.