Artificial Intelligence
Exemplar based knowledge acquisition: a unified approach to concept representati on, classification, and learning
Instance-Based Learning Algorithms
Machine Learning
Why generalize?: Hybrid representations and instance-based learning
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
The Role of Prototypicality in Exemplar-Based Learning
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Abstractions of Data and Time for Multiparametric Time Course Prognoses
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Adaptation and Abstraction in a Case-Based Antibiotics Therapy Adviser
AIME '95 Proceedings of the 5th Conference on Artificial Intelligence in Medicine in Europe: Artificial Intelligence Medicine
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
A Case-Based Approach for the Classification of Medical Time Series
ISMDA '01 Proceedings of the Second International Symposium on Medical Data Analysis
Artificial Intelligence in Medicine
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In this paper we discuss the importance to create prototypes automatically within Case-Based Reasoning systems. We present some general ideas about prototypes deduced from analyses of our experiences with prototype designs in domain specific medical CBR systems. Four medical Case-Based Reasoning systems are described. As they use prototypes for different purposes, the gained improvement is different as well. Furthermore, we claim that the generation of prototypes is an adequate technique to learn the intrinsic case knowledge, especially if the domain theory is weak.