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» Practical Preference Relations for Large Data Sets
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KDD
2008
ACM
110views Data Mining» more  KDD 2008»
14 years 8 months ago
Mining preferences from superior and inferior examples
Mining user preferences plays a critical role in many important applications such as customer relationship management (CRM), product and service recommendation, and marketing camp...
Bin Jiang, Jian Pei, Xuemin Lin, David W. Cheung, ...
ANOR
2008
69views more  ANOR 2008»
13 years 8 months ago
NP-hardness results for the aggregation of linear orders into median orders
Abstract Given a collection of individual preferences defined on a same finite set of candidates, we consider the problem of aggregating them into a collective preference minimizin...
Olivier Hudry
PODS
2006
ACM
95views Database» more  PODS 2006»
14 years 8 months ago
Randomized computations on large data sets: tight lower bounds
We study the randomized version of a computation model (introduced in [9, 10]) that restricts random access to external memory and internal memory space. Essentially, this model c...
André Hernich, Martin Grohe, Nicole Schweik...
BMCBI
2008
133views more  BMCBI 2008»
13 years 8 months ago
A Web-based and Grid-enabled dChip version for the analysis of large sets of gene expression data
Background: Microarray techniques are one of the main methods used to investigate thousands of gene expression profiles for enlightening complex biological processes responsible f...
Luca Corradi, Marco Fato, Ivan Porro, Silvia Scagl...
IJPRAI
2000
83views more  IJPRAI 2000»
13 years 8 months ago
Practical Issues in Modeling Large Diagnostic Systems with Multiply Sectioned Bayesian Networks
As Bayesian networks become widely accepted as a normative formalism for diagnosis based on probabilistic knowledge, they are applied to increasingly larger problem domains. These...
Yanping Xiang, Kristian G. Olesen, Finn Verner Jen...