Adaptation knowledge from the case base

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

Volumn 4830 LNAI, Issue , 2007, Pages 579-588

  Main, Julie ^a   Dillon, Tharam S ^a   Witten, Mary ^a  

^a LA TROBE UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

CASE BASED REASONING; DATA MINING; NEURAL NETWORKS;

CASE ADAPTATION; FOOTWEAR DESIGN PROBLEMS;

KNOWLEDGE ACQUISITION;

EID: 38349057649     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-76928-6_59     Document Type: Conference Paper

References (13)

1. Main, J., Dillon, T., Shiu, S.: A tutorial on case based reasoning. In: Soft Computing in Case Based Reasoning, pp. 1-28. Springer, Heidelberg (2001)
2. Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications 7(1), 39-59 (1994)
3. Kolodner, J.: An introduction to case-based reasoning. Artificial Intelligence Review 6(1), 3-34 (1992)
4. Leake, D.: Becoming an expert case-based reasoner: Learning to adapt prior cases. In: Proceedings of the Eighth Annual Florida Artificial Intelligence Research Symposium, pp. 112-116 (1995)
5. Anand, S., Patterson, D., Hughes, J., Bell, D.: Discovering case knowledge using data mining. In: Wu, X., Kotagiri, R., Korb, K.B. (eds.) PAKDD 1998. LNCS, vol. 1394, pp. 25-35. Springer, Heidelberg (1998)
6. Corchado, J., Lees, B., Fyfe, C., Rees, N., Aiken, J.: Neuro-adaptation method for a case-based reasoning system. Computing and Information Systems 5(1), 15-20 (1998)
7. Lofty, E., Mohamed, A.: Applying neural networks in case-based reasoning adaptation for cost assessment of steel buildings. International Journal of Computers & Applications 24, 28- 38 (2003)
8. Salem, A.B., Nagaty, K., Bagoury, B.E.: A hybrid case-based adaptation model for thyroid cancer diagnosis. In: ICEIS 2003, vol. 2, pp. 58-65 (2003)
9. Swingler, K.: Applying Neural Networks: A Practical Guide. Academic Press Inc., London (1996)
10. Zhou, X., Dillon, T.: A statistical-heuristic feature selection criterion for decision tree induction. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(8), 834-841 (1991)
11. Sestito, S., Dillon, T.: Automated Knowledge Acquisition. Prentice Hall, Englewood Cliffs (1994)
12. Main, J., Dillon, T.: A hybrid case-based reasoner for footwear design. In: Althoff, K.-D., Bergmann, R., Branting, L.K. (eds.) ICCBR 1999. LNCS (LNAI), vol. 1650, pp. 499-509. Springer, Heidelberg (1999)
13. Hanney, K., Keane, M.: The adaptation knowledge bottleneck: How to ease it by learning from cases. In: Leake, D.B., Plaza, E. (eds.) ICCBR 1997. LNCS, vol. 1266, pp. 359-370. Springer, Heidelberg (1997)