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1999
IEEE

Advanced Metrics for Class-Driven Similarity Search

11 years 5 months ago
Advanced Metrics for Class-Driven Similarity Search
This paper presents two metrics for the Nearest Neighbor Classifier that share the property of being adapted, i.e. learned, on a set of data. Both metrics can be used for similarity search when the retrieval critically depends on a symbolic target feature. The first one is called Local Asymmetrically Weighted Similarity Metric (LASM) and exploits reinforcement learning techniques for the computation of asymmetric weights. Experiments on benchmark datasets show that LASM maintains good accuracy and achieves high compression rates outperforming competitor editing techniques like Condensed Nearest Neighbor. On a completely different perspective the second metric, called Minimum Risk Metric (MRM) is based on probability estimates. MRM can be implemented using different probability estimates and performs comparably to the Bayes classifier based on the same estimates. Both LASM and MRM outperform the NN classifier with the Euclidean metric.
Paolo Avesani, Enrico Blanzieri, Francesco Ricci
Added 03 Aug 2010
Updated 03 Aug 2010
Type Conference
Year 1999
Where DEXAW
Authors Paolo Avesani, Enrico Blanzieri, Francesco Ricci
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