Communications of the ACM - Special issue on parallelism
Incremental, instance-based learning of independent and graded concept descriptions
Proceedings of the sixth international workshop on Machine learning
Relational instance-based learning with lists and terms
Machine Learning - Special issue on inducive logic programming
Scope Classification: An Instance-Based Learning Algorithm with a Rule-Based Characterisation
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Rule-Based Similarity Measure
EWCBR '93 Selected papers from the First European Workshop on Topics in Case-Based Reasoning
Cases as terms: A feature term approach to the structured representation of cases
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Distance Induction in First Order Logic
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
An Efficient Metric for Heterogeneous Inductive Learning Applications in the Attribute-Value Language
Rule induction and instance-based learning a unified approach
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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This paper presents an unified framework for the definition of similarity measures for various formalisms (attribute-value, first order logic...). The underlying idea is that the similarity between two objects does not depend only on the attribute values of the objects, but more especially on the set of the potentially relevant definitions of concepts for the problem considered. In our framework, the user defines a language with a grammar to specify the similarity measure. Each term of the language represents a property of the objects. The similarity between two objects is the probability that these two objects both satisfy or both reject simultaneously the properties of the given language. When this probability is not computable, we use a stochastic generation procedure to approximate it. This measure can be applied for both clustering and classification tasks. The empirical evaluation on common classification problems shows a very good accuracy.