Context Based Covariance for Supervised Learning
نویسندگان
چکیده
This paper proposes a new metric, called Context Based Covariance, to capture contextual information intrinsic to multivariate data. Based on this concept, a minimum distance classifier is designed, and its applicability to the domain of supervised machine learning is discussed. The performance of the proposed metric is compared with conventional minimum distance classifiers based on Mahalanobis distance and Euclidean distance.
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