Generalized Variable-Kernel Similarity Metric Learning
Proximity-based classifiers such as RBF-networks andnearest-neighbour classifiers are notoriously sensitive to the metric used to determine distance between samples. In this paper a method for learning such a metric from training data is presented. This algorithm is a generalization of the so called Variable-Kernel Similarity Metric (VSM) Learning, originally proposed by Lowe and is therefore known as Generalized Variable-Kernel Similarity Metric (GVSM) learning. Experimental results show GVSM to be superior to VSM for extremely noisy or cross-correlated data.
KeywordsGeneralization Performance Neighbour Method Cross Validation Error Query Vector Training Observation
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