Communications of the ACM - Special issue on parallelism
Similarity and analogical reasoning
Similarity and analogical reasoning
Case-based reasoning
A Lattice Machine Approach to Automated Casebase Design: Marrying Lazy and Eager Learning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Neighbourhood Counting Metric for Sequences
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Neighborhood counting for financial time series forecasting
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Counting all common subsequences to order alternatives
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
A time weighted neighbourhood counting similarity for time series analysis
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Specific-class distance measures for nominal attributes
AI Communications
Lattice Machine Classification based on Contextual Probability
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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Arguably, analogy is one of the most important aspects of intelligent reasoning. It has been hypothesized that, given suitable background knowledge, analogy can be viewed as a logical inference process. This study follows another school of thought that argues that similarity can provide a probabilistic basis for inference and analogy. Most similarity measures (which are frequently viewed as being conceptually equivalent to distance measures) are restricted to either nominal or ordinal attributes, and some are confined to classification tasks. This paper proposes a flexible similarity measure that is task-independent and applies to both nominal and ordinal data in a conceptually uniform way. The proposed similarity measure is derived from a probability function and corresponds to the intuition that if we consider all neighborhoods around a data point, the data points closer to this point should be included in more of these neighborhoods than more distant points. Experiments we have conducted to demonstrate the usefulness of this measure indicate that it fares very competitively with commonly used similarity measures.