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MLDM
2005
Springer
15 years 12 months ago
Using Clustering to Learn Distance Functions for Supervised Similarity Assessment
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...
Christoph F. Eick, Alain Rouhana, Abraham Bagherje...
KDD
2007
ACM
244views Data Mining» more  KDD 2007»
16 years 6 months ago
A Recommender System Based on Local Random Walks and Spectral Methods
In this paper, we design recommender systems for weblogs based on the link structure among them. We propose algorithms based on refined random walks and spectral methods. First, w...
Zeinab Abbassi, Vahab S. Mirrokni
SDM
2011
SIAM
232views Data Mining» more  SDM 2011»
14 years 9 months ago
A Sequential Dual Method for Structural SVMs
In many real world prediction problems the output is a structured object like a sequence or a tree or a graph. Such problems range from natural language processing to computationa...
Shirish Krishnaj Shevade, Balamurugan P., S. Sunda...
PR
2006
164views more  PR 2006»
15 years 6 months ago
Locally linear metric adaptation with application to semi-supervised clustering and image retrieval
Many computer vision and pattern recognition algorithms are very sensitive to the choice of an appropriate distance metric. Some recent research sought to address a variant of the...
Hong Chang, Dit-Yan Yeung
WEBI
2004
Springer
15 years 12 months ago
An Integrated Approach to Discovery in Complex Information Spaces
As the amount of available data continues to increase, more and more effective means for discovering important patterns and relationships within that data are required. Although t...
Daryl H. Hepting, Cory J. Butz