Hybrid approaches for case retrieval and adaptation

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2821, Issue , 2003, Pages 297-311

  Policastro, Claudio A ^a   Carvalho, Andre C P L F ^a   Delbem, Alexandre C B ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION RETRIEVAL; KNOWLEDGE REPRESENTATION; LEARNING SYSTEMS; NEURAL NETWORKS; PROBLEM SOLVING; REAL TIME SYSTEMS; ARTIFICIAL INTELLIGENCE; COGNITIVE SYSTEMS; INTELLIGENT SYSTEMS;

CASE BASED REASONING (CBR); CASE RETRIEVAL; HYBRID MODELS (HM); INTELLIGENT SYSTEMS; COGNITIVE MODEL; HYBRID APPROACH; HYBRID MECHANISMS; HYBRID MODEL; NEW CASE;

ARTIFICIAL INTELLIGENCE; CASE BASED REASONING;

EID: 9444293356     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-39451-8_22     Document Type: Conference Paper

References (28)

1. Aamondt,A., Plaza,E.: Case based reasoning: Foundational issues, methodological variations, and systems approaches. AI Communications. 7 (1994) 39-59
2. Alexandre,F., Labbi,A., Lallement,Y., Malek,M.: A common architeture for integrating case-based reasoning and neural networks. Technical report, MIX/WP2/IMAG-INRIA/S3. (1996)
3. Bailey,T. Elkan,C.: Estimating the Accuracy of Learned Concepts. 13th International Joint Conference on Artificial Intelligence, Chambéry, France. Bajcsy,R. eds. Morgan Kaufmann. (1993) 895-901
4. Braga,A., Ludermir,T. Carvalho,A.: Sistemas neurais hfbridos. Redes Neurais Artificiais: Teoria e aplicacoes. Livros Tecnicos e Cientificos. (2000)
5. Carpenter,G., Grossberg,S.: ART 2: Self-organization of stable category recognition codes for analog input paterns. Applied Optics. 26:23 (1987) 4919-4930
6. Corchado,J., Lees,B., Fyle,C., Ress,N., Aiken,J.: Neuro-adaptation method for a case-based reasoning system. Computing and Information Systems Journal. 5 (1998) 15-20
7. Cristmann,R.: Estatística aplicada. Edgard Blücher. (1978)
8. Domingos,P.: Unifying Instance-Based and Rule-Based Induction. Machine Learning. 24 (1996) 141-168
9. Duda,R., hart,P., Stork,D.: Pattern Classification. Wiley-Interscience. (2001)
10. Freund,Y. and Schapire,R.: Experiments with a New Boosting Algorithm. 13th. International Conference on Machine Learning, Bari, Italy. Saitta, L. eds. Morgan Kaufmann. (1996) 148-156
11. Hanney,K.: Learning adaptation rules from cases. Master's thesis. University College Dublin. (1996)
12. Hilario,M.: An Overview Of Strategies For Neurosymbolic Integration. Connectionist-Symbolic Integration: From Unified to Hybrid Approaches. Chapter 2. Lawrence Earlbaum Associates, Inc. (1997)
13. Kolodner,J.: An introduction to case based reasoning. AI Review. 6 (1992) 3-34.
14. Kolodner,J.: Adaptation methods and strategies. Case-Based Reasoning. Chapter 11. Morgan Kaufmann. (1993)
15. Leake,D.: CBR in context: The present and future. Case-Based Reasoning: Experiences, Lessons and Future Directions. Chapter 1. AAAI Press/MIT Press. (1996)
16. Lenz,M., Burkhard,H.-D.: Case Retrieval Nets: Basic Ideas and Extensions. 4th. German Workshop on Case-Based Reasoning: System Development and Evaluation, Berlin, German. Burkhard,H.-D. and Lenz,M. eds. (1996) 103-110
17. Malek,M.: A connectionist indexing aproach for CBR systems. 1st International Conference on Case-Based Reasoning. Sesimbra, Portugal. Veloso,M., Aamodt,A. eds. Springer Verlag (1995) 520-527
18. Malek,M.: Hybrid approaches for integrating neurai networks and case-based reasoning: From loosely coupled to tightly coupled models. Soft Computing in Case Based Reasoning. Chapter 4. Springer Verlag. (2001)
19. Mason,R., Gunst,R., Hess,J.: Statistical design and analysis of experiments. John Wiley & Sons. (1989)
20. Orr,M.: Introduction to radial basis function networks, Technical report. Centre for Cognitive Science. University of Edinburgh. (1996).
21. Quinlan,R.: Learning with Continuous Classes. 5th. Australian Joint Conference on Artificial Inteligence, Hobart, Tasmania, World Scientific, Singapore. (1992) 343-348
22. Reategui,E., Campbell,J.: A classification system for credit card transaction. 2th European Workshop on Case-Based Reasoning. Chantilly, France. Keane, M. ed. Springer Verlag. (1994) 280-291
23. Smyth,B., Cunningham,P.: Complexity of adaptation in real-world case-based reasoning systems. 6th Irish Conference on Artificial Intelligence and Cognitive Science. Belfast, Ireland. (1993)
24. Sovat, R. Carvalho,A.: Retrieval and adaptation of cases using an neural network. 4th International Conference on Case-Based Reasoning. Workshop. Vancouver, Canada. (2001) 196-200
25. Vapnik,V.: Statistical learning theory. John Wiley & Sons. (1998)
26. Wang,Y., Witten,I.: Induction of model trees for predicting continuous classes. 9th European Conference on Machine Learning. Prague, Czech Republic. Someren,M., Widmer,G. eds. Springer Verlag. (1997) 128-137
27. Watson,I.: CBR is a methodology not a technology. Knowledge-Based Systems. 12(1999) 303-308
28. Wiratunga,N., Craw,S., Rowe,R.: Learning to adapt for case-based design. 6th European Conference on Case-Based Reasoning. Aberdeen, Scotland, Uk. Craw,S., Preece,A. eds. Springer Verlag. (2002) 421-435