34 search results - page 1 / 7 » Using Clustering to Learn Distance Functions for Supervised ... |
MLDM
2005
Springer
13 years 10 months ago
2005
Springer
Assessing the similarity between objects is a prerequisite for many data mining techniques. This paper introduces a novel approach to learn distance functions that maximizes the c...
KDD
2004
ACM
14 years 5 months ago
2004
ACM
Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clu...
COLING
2008
13 years 6 months ago
2008
In this paper we propose a new distance function (rank distance) designed to reflect stylistic similarity between texts. To assess the ability of this distance measure to capture ...
ICPR
2010
IEEE
13 years 12 months ago
2010
IEEE
This paper introduces a semi-supervised distance metric learning algorithm which uses pair-wise equivalence (similarity and dissimilarity) constraints to improve the original dist...
ICML
2007
IEEE
14 years 5 months ago
2007
IEEE
We consider the problem of learning a similarity function from a set of positive equivalence constraints, i.e. 'similar' point pairs. We define the similarity in informa...