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RSFDGRC
2005
Springer
110views Data Mining» more  RSFDGRC 2005»
15 years 10 months ago
A Rough Set Based Model to Rank the Importance of Association Rules
Abstract. Association rule algorithms often generate an excessive number of rules, many of which are not significant. It is difficult to determine which rules are more useful, int...
Jiye Li, Nick Cercone
SIAMSC
2011
219views more  SIAMSC 2011»
14 years 11 months ago
Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian
We consider the problem of estimating the uncertainty in large-scale linear statistical inverse problems with high-dimensional parameter spaces within the framework of Bayesian inf...
H. P. Flath, Lucas C. Wilcox, Volkan Akcelik, Judi...
AAAI
2012
13 years 7 months ago
Learning the Kernel Matrix with Low-Rank Multiplicative Shaping
Selecting the optimal kernel is an important and difficult challenge in applying kernel methods to pattern recognition. To address this challenge, multiple kernel learning (MKL) ...
Tomer Levinboim, Fei Sha
SIGIR
2003
ACM
15 years 9 months ago
Exploiting query history for document ranking in interactive information retrieval
In this poster,we incorporate user query history, as context information, to improve the retrieval performance in interactive retrieval. Experiments using the TREC data show that ...
Xuehua Shen, ChengXiang Zhai
178
Voted
ECWEB
2010
Springer
155views ECommerce» more  ECWEB 2010»
15 years 3 months ago
Partial Ranking of Products for Recommendation Systems
A recommendation system (or recommender) is an algorithm whose goal is to recommend products to potential users. To achieve its task, it uses information about some user preference...
Sébastien Hémon, Thomas Largillier, ...