Using Decision Trees to Improve Case-Based Learning

Proceedings of the 10th International Conference on Machine Learning, ICML 1993

Volumn , Issue , 1993, Pages 25-32

  Cardie, Claire ^a  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING; LEARNING ALGORITHMS; MACHINE LEARNING; NATURAL LANGUAGE PROCESSING SYSTEMS; NEAREST NEIGHBOR SEARCH;

CASE BASED LEARNING; CLASSIFICATION TASKS; CONCEPTUAL CLUSTERING; DECISION-TREE ALGORITHM; EXPERT KNOWLEDGE; HYBRID APPROACH; MACHINE LEARNING SYSTEMS; PERFORMANCE; PERFORMANCE TASKS; SETS OF FEATURES;

DECISION TREES;

EID: 33845629152     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. Aha, D., Kibler, D., & Albert, M. (1991). Instance- Based Learning Algorithms. Machine Learning 6 (1): pp. 37-66.
2. Aha, D. (1989). Incremental, Instance-Based Learning of Independent and Graded Concept Descriptions. Proceedings, Sixth International Workshop on Machine Learning, pp. 387-391. Cornell University, Ithaca, NY. Morgan Kaufinann.
3. Almuallim, H., & Dietterich, T. G. (1991). Learning With Many Irrelevant Features. Proceedings, Ninth National Conference on Artificial Intelligence, pp. 547- 552. Anaheim, CA. AAAI Press / The MIT Press.
4. Berwick, R. (1983). Learning word meanings from examples. Proceedings, Eighth International Joint Conference on Artificial Intelligence, pp. 459-461. Karlsruhe, Germany.
5. Brent, M. (1991). Automatic acquisition of subcategorization frames from untagged text. Proceedings, 29th Annual Meeting of the Association for Computational Linguistics, pp. 209-214. University of California, Berkeley. Association for Computational Linguistics.
6. Brent, M. (1990). Semantic classification of verbs from their syntactic contexts: automated lexicography with implications for child language acquisition. Proceedings, Twelfth Annual Conference of the Cognitive Science Society, pp. 428-437. Cambridge MA. The Cognitive Science Society.
7. Cardie, C. (1993). A Case-Based Approach to Knowledge Acquisition for Domain-Specific Sentence Analysis. To appear in Proceedings, Eleventh National Conference on Artificial Intelligence. Washington, DC.
8. Church, K., & Hanks, P. (1990). Word association norms, mutual information, and lexicography. Computational Linguistics, 16.
9. Fisher, D. (1989). Noise-Tolerant Conceptual Clustering. Proceedings, Eleventh International Joint Conference on Artificial Intelligence, pp. 630-635. Detroit MI. Morgan Kaufmann.
10. Fisher, D. H. (1987). Knowledge Acquisition Via Incremental Conceptual Clustering. Machine Learning 2: 139-172.
11. Granger, R. (1977). Foulup: A program that figures out meanings of words from context. Proceedings, Fifth International Joint Conference on Artificial Intelligence, pp. 172-178. Morgan Kaufmann.
12. Grefenstette, G. (1992). SEXTANT: Exploring unexplored contexts for semantic extraction from syntactic analysis. Proceedings, SOth Annual Meeting of the Association for Computational Linguistics, pp. 324-326.University of Delaware, Newark, DE. Association for Computational Linguistics.
13. Hastings, P., Lytinen, S., & Lindsay, R. (1991). Learning Words from Context. Proceedings, Eighth International Conference on Machine Learning, pp. 55-59.Northwestern University, Chicago, IL. Morgan Kaufmann.
14. Hindle, D. (1990). Noun classification from predicateargument structures. Proceedings, 28th Annual Meeting of the Association for Computational Linguistics, pp. 268-275. University of Pittsburgh. Association for Computational Linguistics.
15. Jacobs, P., & Zernik, U. (1988). Acquiring Lexical Knowledge from Text: A Case Study. Proceedings, Seventh National Conference on Artificial Intelligence, pp. 739-744. St. Paul, MN. Morgan Kaufmann.
16. Lehnert, W . (1990). Symbolic/Subsymbolic Sentence Analysis: Exploiting the Best of Two Worlds. In J. Barnden, & J. Pollack (Eds.), Advances in Connectionist and Neural Computation Theory, pp. 135-164. Norwood, NJ: Ablex Publishers.
17. Lytinen, S., & Roberts, S. (1989). Lexical Acquisition as a By-Product of Natural Language Processing. Proceedings, IJCAI-89 Workshop on Lexical Acquisition.
18. Michalski, R. S., & Stepp, R. (1983). Learning from observation: conceptual clustering. In R. S. Michalski, J. G. Carbonell, k T. M. Mitchell (Eds.), Machine Learning: An Artificial Intelligence Approach. Morgan Kaufmann.
19. Quinlan, J. R. (1992). Programs for Machine Learning. Morgan Kaufmann.
20. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1: 81-106.
21. Quinlan, J. R. (1983). Learning EflScient Classification Procedures and Their Application to Chess End Games. In R. S. Michalski, J. G. Carbonell, k T. M. Mitchell (Eds.), Machine Learning: An Artificial Intelligence Approach. Palo Alto: Morgan Kaufmann.
22. Resnik, P. (1992). A class-based approach to lexical discovery. Proceedings, SOth Annual Meeting of the Association for Computational Linguistics, pp. 327- 329. University of Delaware, Newark, DE. Association for Computational Linguistics.
23. Selfridge, M. (1986). A computer model of child language learning. Artificial Intelligence, 29: 171-216.
24. Skalak, D. and Rissland, (1990). Inductive Learning in a Mixed Paradigm Setting. Proceedings, Eighth National Conference on Artificial Intelligence, pp. 840- 847. Boston, MA. AAAI Press/MIT Press.
25. Wilensky, R. (1991). Extending the Lexicon by Exploiting Subregularities. Tech. Report No. UCB/CSD 91/618. Computer Science Division (EECS), Univ. of California, Berkeley.
26. Yarowsky, D. (1992). Word-Sense Disambiguation Using Statistical Models of Roget's Categories Trained on Large Corpora. Proceedings, COLING-92.
27. Zernik, U. (1991). Trainl vs. Train 2: Tagging Word Senses in Corpus. In U. Zernik (Ed.), Lexical Acquisition: Exploiting On-Line Resources to Build a Lexicon, pp. 91-112. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.