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» Practical Preference Relations for Large Data Sets
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LWA
2004
13 years 10 months ago
Efficient Frequent Pattern Mining in Relational Databases
Data mining on large relational databases has gained popularity and its significance is well recognized. However, the performance of SQL based data mining is known to fall behind ...
Xuequn Shang, Kai-Uwe Sattler, Ingolf Geist
BMCBI
2010
104views more  BMCBI 2010»
13 years 8 months ago
A novel scoring function for discriminating hyperthermophilic and mesophilic proteins with application to predicting relative th
Background: The ability to design thermostable proteins is theoretically important and practically useful. Robust and accurate algorithms, however, remain elusive. One critical pr...
Yunqi Li, C. Russell Middaugh, Jianwen Fang
UAI
2003
13 years 9 months ago
Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes
Collaborative filtering (CF) and contentbased filtering (CBF) have widely been used in information filtering applications, both approaches having their individual strengths and...
Kai Yu, Anton Schwaighofer, Volker Tresp, Wei-Ying...
CSCW
2002
ACM
13 years 8 months ago
Who can claim complete abstinence from peeking at print jobs?
While systems supporting communities of practice in work organizations have been shown to be desirable many, if not all, are decoupled from daily work practices and tools. This hi...
Antonietta Grasso, Jean-Luc Meunier
ICML
2008
IEEE
14 years 9 months ago
Fully distributed EM for very large datasets
In EM and related algorithms, E-step computations distribute easily, because data items are independent given parameters. For very large data sets, however, even storing all of th...
Jason Wolfe, Aria Haghighi, Dan Klein