A Comparative Evaluation of Voting and Meta-learning on Partitioned Data

Proceedings of the 12th International Conference on Machine Learning, ICML 1995

Volumn , Issue , 1995, Pages 90-98

  Chan, Philip K ^a   Stolfo, Salvatore J ^a  

^a Och Spine at New York Presbyterian Hospitals   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA HANDLING; LEARNING SYSTEMS;

COMPARATIVE EVALUATIONS; DATA SET; INDUCTIVE LEARNING; LARGER NETWORKS; LEARNING PROBLEM; MAIN-MEMORY; METALEARNING; NETWORK COMPUTING; ORDERS OF MAGNITUDE; REAL-WORLD;

LEARNING ALGORITHMS;

EID: 85152630265     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Aha, D. & Kibler, D. (1989). Noise-tolerant instancebased learning algorithms. Proc. IJCAI-89 (pp. 794- 799).
2. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Belmont, CA: Wadsworth.
3. Buntine, W. & Caruana, R. (1991). Introduction to IND and Recursive Partitioning. NASA Ames Research Center. Catlett, J. (1991). Megainduction: A test flight. Proc. Eighth Intl. Work. Machine Learning (pp. 596-599).
4. Chan, P. & Stolfo, S. (1993a). Experiments on multistrategy learning by meta-learning. Proc. Second Intl. Conf. Info. Know. Manag, (pp. 314-323).
5. Chan, P. & Stolfo, S. (1993b). Meta-learning for multistrategy and parallel learning. Proc. Second Intl. Work. on Multistrategy Learning (pp. 150-165).
6. Chan, P. & Stolfo, S. (1993c). Toward multistrategy parallel and distributed learning in sequence analysis. Proc. First Intl. Conf. Intel. Sys. Mol. Biol. (pp. 65-73).
7. Chan, P. & Stolfo, S. (1993d). Toward parallel and distributed learning by meta-learning. Working Notes AAAI Work. Know. Disc. Databases (pp. 227-240).
8. Chan, P. & Stolfo, S. (1995). Learning arbiter and combiner trees from partitioned data for scaling machine learning. Proc. Intl. Conf. Knowledge Discovery and Data Mining. To appear.
9. Craven, M. & Shavlik, J. (1993). Learning to represent codons: A challenge problem for constructive induction. Proc. IJCAI-93 (pp. 1319-1324).
10. Littlestone, N. & Warmuth, M. (1989). The weighted majority algorithm. (Technical Report UCSC-CRL-89- 16): Univ. Cal., Santa Cruz.
11. Quinlan, J. R. (1979). Induction over large data bases. (Technical Report STAN-CS-79-739): Comp. Sei. Dept., Stanford Univ.
12. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81-106.
13. Schapire, R. (1990). The strength of weak learnability. Machine Learning, 5, 197-226.
14. Towell, G., Shavlik, J., & Noordewier, M. (1990). Refinement of approximate domain theories by knowledgebased neural networks. Proc. AAAI-90 (pp. 861-866).
15. Utgoff, P. (1989). Incremental induction of decision trees. Machine Learning, A, 161-186.
16. Wah, B. (1993). High performance computing and communications for grand challenge applications: Computer vision, speech and natural language processing, and artificial intelligence. IEEE Trans. Know. Data. Eng., 5(1), 138-154.
17. Wirth, J. & Catlett, J. (1988). Experiments on the costs and benefits of windowing in ID3. Proc. Fifth Intl. Conf. Machine Learning (pp. 87-99).
18. Wolpert, D. (1992). Stacked generalization. Neural Networks, 5,241-259.
19. Xu, L., Krzyzak, A., & Suen, C. (1992). Methods of combining multiple classifires and their applications to handwriting recognition. IEEE Trans. Sys. Man. Cyb., 22,418-435.
20. Zhang, X., Mckenna, M., Mesirov, J., & Waltz, D. (1989). An Efficient Implementation of the Backpropagation Algorithm on the Connection Machine CM-2.(Technical Report RL89-1): Thinking Machines Corp.
21. Zhang, X., Mesirov, J., & Waltz, D. (1992). A hybrid system for protein secondary structure prediction. J. Mol. Biol., 225, 1049-1063.