Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Saussurian analogy: a theoretical account and its application
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Measuring semantic similarity by latent relational analysis
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning Metrics Between Tree Structured Data: Application to Image Recognition
ECML '07 Proceedings of the 18th European conference on Machine Learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
Handling Analogical Proportions in Classical Logic and Fuzzy Logics Settings
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Journal of Artificial Intelligence Research
Logical proportions: typology and roadmap
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
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This paper deals with learning to classify by using an approximation of the analogical proportion between four objects. These objects are described by binary and nominal attributes. Firstly, the paper recalls what is an analogical proportion between four objects, then it introduces a measure called "analogical dissimilarity", reflecting how close four objects are from being in an analogical proportion. Secondly, it presents an analogical instance-based learning method and describes a fast algorithm. Thirdly, a technique to assign a set of weights to the attributes of the objects is given: a weight is chosen according to the type of the analogical proportion involved. The weights are obtained from the learning sample. Then, some results of the method are presented. They compare favorably to standard classification techniques on six benchmarks. Finally, the relevance and complexity of the method are discussed.