Identifying decision maker’s preferences through a prototype based inductive learning method: A medical case study?

Behaviour and Information Technology

Volumn 15, Issue 2, 1996, Pages 113-122

  Vassilakis, Panayotis ^a   Moustakis, Vassilis ^a,b  

^a TECHNICAL UNIVERSITY OF CRETE   (Greece)

^b INSTITUTE OF COMPUTER SCIENCE   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

BIOMEDICAL ENGINEERING; COMPUTER SIMULATION; HUMAN ENGINEERING; LEARNING SYSTEMS; SOFTWARE PROTOTYPING;

MEDICAL CASE STUDY; PROTOTYPE BASED INDUCTIVE LEARNING METHOD;

DECISION MAKING;

EID: 0030100863     PISSN: 0144929X     EISSN: 13623001     Source Type: Journal    
DOI: 10.1080/014492996120328     Document Type: Article

References (15)

1. Barsalou, L. W. 1989, Intraconcept similarity and its implications for interconcept similarity, in S. Vosniadou and A. Ortony (eds), Similarity and Analogical Reasoning (Cambridge University Press, Cambridge), 76-121.
2. Bisson, G. 1992, Conceptual clustering in a first order representation, Proceedings of the European Conference on Artificial Intelligence (Wiley, Chichester), 458-462.
3. Clark, P. and Niblett, T. 1989, The CN2 Induction Algorithm, Machine Learning, 3, 261-283.
4. Hampton, J. 1993, Prototype models of concept representation, in I. Van Mechelen, J. Hampton, R. S. Michalski and P. Theuns (eds), Categories and Concepts: Theoretical views (Academic Press, London), 67-96.
5. Hampton, J. and Dubois, D. 1993, Psychological models of concepts, in I. van Mechelen, J. Hampton, R. S. Michalski and P. Theuns (eds), Categories and Concepts: Theoretical views (Academic Press, London), 11-34.
6. Hanson, J. H. and Bauer, M. 1989, Conceptual clustering, categorization and polymorphy, Machine Learning, 3, 343-372.
7. Hoffman, P. I. 1960, The paramorphic representation of clinical judgement, Psychological Bulletin, 57, 116-131.
8. Michalski, R. S. 1993, Beyond prototypes and frames: the two tiered concept representation, in I. van Mechelen, J. Hampton, R. S. Michalski and P. Theuns (eds), Categories and Concepts: Theoretical views (Academic Press, London), 145-172.
9. Michalski, R. S. 1990, Learning flexible concepts: Fundamental Ideas and Methods Based on Two-Tiered Representation, in Y. Kodratoff and R. S. Michalski (eds.), Machine Learning: An Artificial Intelligence Approach (Vol. III), (Morgan Kaufmann, San Mateo, California), 63-111.
10. Michalski, R. S. 1989, Two-tiered concept meaning, inferential matching and conceptual cohesiveness, in S. Vosniadou and A. Ortony (eds), Similarity and Analogical Reasoning (Cambridge University Press, London), 122-145.
11. Michalski, R. S. 1983, A theory and methodology of inductive learning, in R. S. Michalski, J. G. Carbonnell and T. M. Mitchell (eds), Machine Learning: An artificial intelligence approach, Vol. I (Tioga Publishers, Palo Alto, California), 83-134.
12. Quinlan, J. R. 1986, Induction of decision trees, Machine Learning, 1, 81-106.
13. Rosch, E. and Mervis, C. B. 1975, Family resemblances: studies in the internal structure of categories, Cognitive Psychology, 7, 573-605.
14. Simon, H. A. 1978a, On how to decide what to do, Bell Journal of Economics, 9, 497-507.
15. Simon, H. A. 1978b, Rationality as process and product of thought, American Economic Review, 79, 1-16.