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 similari...
Recent advances in large-margin classification of data residing in general metric spaces (rather than Hilbert spaces) enable classification under various natural metrics, such as ...
Lee-Ad Gottlieb, Leonid Kontorovich, Robert Krauth...
Metric distances and the more general concept of dissimilarities are widely used tools in instance-based learning methods and very especially in the nearestneighbor classification...
Metric learning is a fundamental problem in computer vision. Different features and algorithms may tackle a problem from different angles, and thus often provide complementary inf...
Bo Wang, Jiayan Jiang, Wei Wang 0028, Zhi-Hua Zhou...
Abstract. A new, exemplar-based, probabilistic paradigm for visual tracking is presented. Probabilistic mechanisms are attractive because they handle fusion of information, especia...