Learning application-specific distance metrics from labeled data is critical for both statistical classification and information retrieval. Most of the earlier work in this area h...
We consider online routing algorithms for finding paths between the vertices of plane graphs. We show (1) there exists a routing algorithm for arbitrary triangulations that has no...
Prosenjit Bose, Pat Morin, Andrej Brodnik, Svante ...
Existing niching techniques commonly use the Euclidean distance metric in the decision space for the classification of feasible solutions to the niches under formation. This approa...
We investigate the complexity and approximability of some location problems when two distance values are specified for each pair of potential sites. These problems involve the se...
Venkatesh Radhakrishnan, Sven Oliver Krumke, Madha...
The facility design problem is a common one in manufacturing and service industries and has been studied extensively in the literature. However, restrictions on the scope of the de...
Bryan A. Norman, Alice E. Smith, Rifat Aykut Arapo...
Abstract. In this paper we propose a new distance metric for probability density functions (PDF). The main advantage of this metric is that unlike the popular Kullback-Liebler (KL)...
Distance metric is widely used in similarity estimation. In this paper we find that the most popular Euclidean and Manhattan distance may not be suitable for all data distribution...
In this paper we propose to use a distance metric based on user-preferences to efficiently find solutions for many-objective problems. In a user-preference based algorithm a decis...
This paper considers a method for learning a distance metric in a fingerprinting system which identifies a query content by measuring the distance between the fingerprint of th...
This paper introduces a semi-supervised distance metric learning algorithm which uses pair-wise equivalence (similarity and dissimilarity) constraints to improve the original dist...