A low-distortion embedding between two metric spaces is a mapping which preserves the distances between each pair of points, up to a small factor called distortion. Low-distortion...
Mihai Badoiu, Julia Chuzhoy, Piotr Indyk, Anastasi...
In high-dimensional and complex metric spaces, determining the nearest neighbor (NN) of a query object ? can be a very expensive task, because of the poor partitioning operated by...
We address the problem of autonomously learning controllers for visioncapable mobile robots. We extend McCallum's (1995) Nearest-Sequence Memory algorithm to allow for genera...
Viktor Zhumatiy, Faustino J. Gomez, Marcus Hutter,...
This paper proposes the concept of potential slack and show it is an effective metric of combinational circuit performance. We provide several methods for estimating potential sla...
This paper presents a method for learning a distance metric from relative comparison such as “A is closer to B than A is to C”. Taking a Support Vector Machine (SVM) approach,...