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SIGMOD
2007
ACM
181views Database» more  SIGMOD 2007»
14 years 7 months ago
Progressive and selective merge: computing top-k with ad-hoc ranking functions
The family of threshold algorithm (i.e., TA) has been widely studied for efficiently computing top-k queries. TA uses a sort-merge framework that assumes data lists are pre-sorted...
Dong Xin, Jiawei Han, Kevin Chen-Chuan Chang
CIKM
2006
Springer
13 years 10 months ago
Ranking robustness: a novel framework to predict query performance
In this paper, we introduce the notion of ranking robustness, which refers to a property of a ranked list of documents that indicates how stable the ranking is in the presence of ...
Yun Zhou, W. Bruce Croft
SIGIR
2012
ACM
11 years 9 months ago
Robust ranking models via risk-sensitive optimization
Many techniques for improving search result quality have been proposed. Typically, these techniques increase average effectiveness by devising advanced ranking features and/or by...
Lidan Wang, Paul N. Bennett, Kevyn Collins-Thompso...
ISSAC
2005
Springer
105views Mathematics» more  ISSAC 2005»
14 years 8 days ago
Computing the rank and a small nullspace basis of a polynomial matrix
We reduce the problem of computing the rank and a nullspace basis of a univariate polynomial matrix to polynomial matrix multiplication. For an input n×n matrix of degree d over ...
Arne Storjohann, Gilles Villard
AAAI
2004
13 years 8 months ago
Error Detection and Impact-Sensitive Instance Ranking in Noisy Datasets
Given a noisy dataset, how to locate erroneous instances and attributes and rank suspicious instances based on their impacts on the system performance is an interesting and import...
Xingquan Zhu, Xindong Wu, Ying Yang